skbio.stats.distance.DistanceMatrix

class skbio.stats.distance.DistanceMatrix(data, ids=None)[source]

Store distances between objects.

A DistanceMatrix is a DissimilarityMatrix with the additional requirement that the matrix data is symmetric. There are additional methods made available that take advantage of this symmetry.

Notes

The distances are stored in redundant (square-form) format 1. To facilitate use with other scientific Python routines (e.g., scipy), the distances can be retrieved in condensed (vector-form) format using condensed_form.

DistanceMatrix only requires that the distances it stores are symmetric. Checks are not performed to ensure the other three metric properties hold (non-negativity, identity of indiscernibles, and triangle inequality) 2. Thus, a DistanceMatrix instance can store distances that are not metric.

References

1

http://docs.scipy.org/doc/scipy/reference/spatial.distance.html

2

http://planetmath.org/metricspace

Attributes

T

Transpose of the dissimilarity matrix.

data

Array of dissimilarities.

default_write_format

dtype

Data type of the dissimilarities.

ids

Tuple of object IDs.

png

Display heatmap in IPython Notebook as PNG.

shape

Two-element tuple containing the dissimilarity matrix dimensions.

size

Total number of elements in the dissimilarity matrix.

svg

Display heatmap in IPython Notebook as SVG.

Built-ins

x in dm

Check if the specified ID is in the dissimilarity matrix.

dm1 == dm2

Compare this dissimilarity matrix to another for equality.

dm[x]

Slice into dissimilarity data by object ID or numpy indexing.

dm1 != dm2

Determine whether two dissimilarity matrices are not equal.

str(dm)

Return a string representation of the dissimilarity matrix.

Methods

between(from_, to_[, allow_overlap])

Obtain the distances between the two groups of IDs

condensed_form()

Return an array of distances in condensed format.

copy()

Return a deep copy of the dissimilarity matrix.

filter(ids[, strict])

Filter the dissimilarity matrix by IDs.

from_iterable(iterable, metric[, key, keys, …])

Create DistanceMatrix from all pairs in an iterable given a metric.

index(lookup_id)

Return the index of the specified ID.

permute([condensed])

Randomly permute both rows and columns in the matrix.

plot([cmap, title])

Creates a heatmap of the dissimilarity matrix

read(file[, format])

Create a new DistanceMatrix instance from a file.

redundant_form()

Return an array of dissimilarities in redundant format.

to_data_frame()

Create a pandas.DataFrame from this DissimilarityMatrix.

to_series()

Create a pandas.Series from this DistanceMatrix.

transpose()

Return the transpose of the dissimilarity matrix.

within(ids)

Obtain all the distances among the set of IDs

write(file[, format])

Write an instance of DistanceMatrix to a file.