skbio.stats.distance.PairwiseMatrix#
- class skbio.stats.distance.PairwiseMatrix(data, ids=None, validate=True)[source]#
Store pairwise relationships between objects.
A PairwiseMatrix instance stores a square, two-dimensional matrix of relationships between objects. Objects could be, for example, samples or DNA sequences. A sequence of IDs accompanies the data.
Methods are provided to load and save pairwise matrices from/to disk, as well as perform common operations such as extracting values based on object ID. Additionally, the
plot
method provides convenient built-in plotting functionality.- Parameters:
- dataarray_like or PairwiseMatrix
Square, two-dimensional
numpy.ndarray
of pairwise relationships (floats), or a structure that can be converted to anumpy.ndarray
usingnumpy.asarray
or a one-dimensional vector of pairwise relationships (floats), as defined by scipy.spatial.distance.squareform. Can instead be a PairwiseMatrix (or subclass) instance, in which case the instance’s data will be used. Data will be converted to a floatdtype
if necessary. A copy will not be made if already anumpy.ndarray
with a floatdtype
.- idssequence of str, optional
Sequence of strings to be used as object IDs. Must match the number of rows/cols in data. If
None
(default), IDs will be monotonically-increasing integers cast as strings, with numbering starting from zero, e.g.,('0', '1', '2', '3', ...)
.- validatebool, optional
If validate is
True
(default) and data is not a PairwiseMatrix object, the input data will be validated.
Notes
The values are stored in redundant (square-form) format [1].
The data are not checked for symmetry or hollowness, nor guaranteed/assumed to be symmetric or hollow.
References
Attributes
Transpose of the matrix.
Array of pairwise relationships.
Data type of the matrix values.
Tuple of object IDs.
Two-element tuple containing the redundant form matrix dimensions.
Total number of elements in the underlying data structure.
Methods
between
(from_, to_[, allow_overlap])Obtain the pairwise values between the two groups of IDs.
copy
()Return a deep copy of the matrix.
filter
(ids[, strict])Filter the matrix by IDs.
from_iterable
(iterable, metric[, key, keys])Create PairwiseMatrix from an iterable given a metric.
index
(lookup_id)Return the index of the specified ID.
plot
([cmap, title])Create a heatmap of the matrix.
read
([format])Create a new
PairwiseMatrix
instance from a file.Return an array of values in redundant form.
rename
(mapper[, strict])Rename IDs in the matrix.
Create a
pandas.DataFrame
from thisPairwiseMatrix
.Return the transpose of the matrix.
within
(ids)Obtain all the pairwise values among the set of IDs.
write
(file[, format])Write an instance of
PairwiseMatrix
to a file.Special methods
__contains__
(lookup_id)Check if the specified ID is in the matrix.
__eq__
(other)Compare this matrix to another for equality.
__getitem__
(index)Slice into data by object ID or numpy indexing.
__ne__
(other)Determine whether two matrices are not equal.
__str__
()Return a string representation of the matrix.
Special methods (inherited)
__ge__
(value, /)Return self>=value.
__getstate__
(/)Helper for pickle.
__gt__
(value, /)Return self>value.
__le__
(value, /)Return self<=value.
__lt__
(value, /)Return self<value.
Details
- data#
Array of pairwise relationships.
A square, two-dimensional
numpy.ndarray
of values (floats). A copy is not returned.Notes
This property is not writeable.
- default_write_format = 'lsmat'#
- dtype#
Data type of the matrix values.
- ids#
Tuple of object IDs.
A tuple of strings, one for each object in the pairwise matrix.
Notes
This property is writeable, but the number of new IDs must match the number of objects in data.
- shape#
Two-element tuple containing the redundant form matrix dimensions.
Notes
As the matrix is guaranteed to be square, both tuple entries will always be equal. The shape of the redundant form matrix is returned.
- size#
Total number of elements in the underlying data structure.
Notes
If the matrix is stored in redundant form, size is equivalent to
self.shape[0] * self.shape[1]
. If the matrix is stored in condensed form, size is equal to the number of elements in the condensed array.
- __contains__(lookup_id)[source]#
Check if the specified ID is in the matrix.
- Parameters:
- lookup_idstr
ID to search for.
- Returns:
- bool
True if lookup_id is in the matrix, False otherwise.
See also
- __eq__(other)[source]#
Compare this matrix to another for equality.
Two matrices are equal if they have the same shape, IDs (in the same order!), and have data arrays that are equal.
Checks are not performed to ensure that other is a PairwiseMatrix instance.
- Parameters:
- otherPairwiseMatrix
Matrix to compare to for equality.
- Returns:
- bool
True
if self is equal to other,False
otherwise.
- __getitem__(index)[source]#
Slice into data by object ID or numpy indexing.
Extracts data from the matrix by object ID, a pair of IDs, or numpy indexing/slicing.
- Parameters:
- indexstr, two-tuple of str, or numpy index
index can be one of the following forms: an ID, a pair of IDs, or a numpy index.
If index is a string, it is assumed to be an ID and a
numpy.ndarray
row vector is returned for the corresponding ID. Note that the ID’s row of values is returned, not its column. If the matrix is symmetric, the two will be identical, but this makes a difference if the matrix is asymmetric.If index is a two-tuple of strings, each string is assumed to be an ID and the corresponding matrix element is returned that represents the value between the two IDs. Note that the order of lookup by ID pair matters if the matrix is asymmetric: the first ID will be used to look up the row, and the second ID will be used to look up the column. Thus,
dm['a', 'b']
may not be the same asdm['b', 'a']
if the matrix is asymmetric.Otherwise, index will be passed through to
PairwiseMatrix.data.__getitem__
, allowing for standard indexing of anumpy.ndarray
(e.g., slicing).
- Returns:
- ndarray or scalar
Indexed data, where return type depends on the form of index (see description of index for more details).
- Raises:
- MissingIDError
If the ID(s) specified in index are not in the matrix.
Notes
The lookup based on ID(s) is quick. NumPy indexing (slicing) on condensed form matrices will convert them to redundant, roughly doubling their memory requirement.