libpysal.weights.KNN

class libpysal.weights.KNN(data, k=2, p=2, ids=None, radius=None, distance_metric='euclidean', **kwargs)[source]

Creates nearest neighbor weights matrix based on k nearest neighbors.

Parameters
kdtreeobject

PySAL KDTree or ArcKDTree where KDtree.data is array (n,k) n observations on k characteristics used to measure distances between the n objects

kint

number of nearest neighbors

pfloat

Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance Ignored if the KDTree is an ArcKDTree

idslist

identifiers to attach to each observation

Returns
wW

instance Weights object with binary weights

See also

libpysal.weights.weights.W

Notes

Ties between neighbors of equal distance are arbitrarily broken.

Examples

>>> import libpysal
>>> import numpy as np
>>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> kd = libpysal.cg.KDTree(np.array(points))
>>> wnn2 = libpysal.weights.KNN(kd, 2)
>>> [1,3] == wnn2.neighbors[0]
True
>>> wnn2 = KNN(kd,2)
>>> wnn2[0]
{1: 1.0, 3: 1.0}
>>> wnn2[1]
{0: 1.0, 3: 1.0}

now with 1 rather than 0 offset

>>> wnn2 = libpysal.weights.KNN(kd, 2, ids=range(1,7))
>>> wnn2[1]
{2: 1.0, 4: 1.0}
>>> wnn2[2]
{1: 1.0, 4: 1.0}
>>> 0 in wnn2.neighbors
False
__init__(self, data, k=2, p=2, ids=None, radius=None, distance_metric='euclidean', **kwargs)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(self, data[, k, p, ids, radius, …])

Initialize self.

asymmetry(self[, intrinsic])

Asymmetry check.

from_WSP(WSP[, silence_warnings])

from_adjlist(adjlist[, focal_col, …])

Return an adjacency list representation of a weights object.

from_array(array, \*args, \*\*kwargs)

Creates nearest neighbor weights matrix based on k nearest neighbors.

from_dataframe(df[, geom_col, ids])

Make KNN weights from a dataframe.

from_file([path, format])

Read a weights file into a W object.

from_networkx(graph[, weight_col])

Convert a networkx graph to a PySAL W object.

from_shapefile(filepath, \*args, \*\*kwargs)

Nearest neighbor weights from a shapefile.

full(self)

Generate a full numpy.ndarray.

get_transform(self)

Getter for transform property.

plot(self, gdf[, indexed_on, ax, color, …])

Plot spatial weights objects.

remap_ids(self, new_ids)

In place modification throughout W of id values from w.id_order to new_ids in all.

reweight(self[, k, p, new_data, new_ids, …])

Redo K-Nearest Neighbor weights construction using given parameters

set_shapefile(self, shapefile[, idVariable, …])

Adding metadata for writing headers of .gal and .gwt files.

set_transform(self[, value])

Transformations of weights.

symmetrize(self[, inplace])

Construct a symmetric KNN weight.

to_WSP(self)

Generate a WSP object.

to_adjlist(self[, remove_symmetric, …])

Compute an adjacency list representation of a weights object.

to_file(self[, path, format])

Write a weights to a file.

to_networkx(self)

Convert a weights object to a networkx graph.

Attributes

asymmetries

List of id pairs with asymmetric weights.

cardinalities

Number of neighbors for each observation.

component_labels

Store the graph component in which each observation falls.

diagW2

Diagonal of \(WW\).

diagWtW

Diagonal of \(W^{'}W\).

diagWtW_WW

Diagonal of \(W^{'}W + WW\).

histogram

Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit.

id2i

Dictionary where the key is an ID and the value is that ID’s index in W.id_order.

id_order

Returns the ids for the observations in the order in which they would be encountered if iterating over the weights.

id_order_set

Returns True if user has set id_order, False if not.

islands

List of ids without any neighbors.

max_neighbors

Largest number of neighbors.

mean_neighbors

Average number of neighbors.

min_neighbors

Minimum number of neighbors.

n

Number of units.

n_components

Store whether the adjacency matrix is fully connected.

neighbor_offsets

Given the current id_order, neighbor_offsets[id] is the offsets of the id’s neighbors in id_order.

nonzero

Number of nonzero weights.

pct_nonzero

Percentage of nonzero weights.

s0

s0 is defined as

s1

s1 is defined as

s2

s2 is defined as

s2array

Individual elements comprising s2.

sd

Standard deviation of number of neighbors.

sparse

Sparse matrix object.

transform

Getter for transform property.

trcW2

Trace of \(WW\).

trcWtW

Trace of \(W^{'}W\).

trcWtW_WW

Trace of \(W^{'}W + WW\).

classmethod from_array(array, *args, **kwargs)[source]

Creates nearest neighbor weights matrix based on k nearest neighbors.

Parameters
arraynp.ndarray

(n, k) array representing n observations on k characteristics used to measure distances between the n objects

**kwargskeyword arguments, see Rook
Returns
wW

instance Weights object with binary weights

See also

libpysal.weights.weights.W

Notes

Ties between neighbors of equal distance are arbitrarily broken.

Examples

>>> from libpysal.weights import KNN
>>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> wnn2 = KNN.from_array(points, 2)
>>> [1,3] == wnn2.neighbors[0]
True
>>> wnn2 = KNN.from_array(points,2)
>>> wnn2[0]
{1: 1.0, 3: 1.0}
>>> wnn2[1]
{0: 1.0, 3: 1.0}

now with 1 rather than 0 offset

>>> wnn2 = KNN.from_array(points, 2, ids=range(1,7))
>>> wnn2[1]
{2: 1.0, 4: 1.0}
>>> wnn2[2]
{1: 1.0, 4: 1.0}
>>> 0 in wnn2.neighbors
False
classmethod from_dataframe(df, geom_col='geometry', ids=None, *args, **kwargs)[source]

Make KNN weights from a dataframe.

Parameters
dfpandas.dataframe

a dataframe with a geometry column that can be used to construct a W object

geom_colstr

column name of the geometry stored in df

idsstr or iterable

if string, the column name of the indices from the dataframe if iterable, a list of ids to use for the W if None, df.index is used.

See also

libpysal.weights.weights.W
classmethod from_shapefile(filepath, *args, **kwargs)[source]

Nearest neighbor weights from a shapefile.

Parameters
datastr

shapefile containing attribute data.

kint

number of nearest neighbors

pfloat

Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance

idslist

identifiers to attach to each observation

radiusfloat

If supplied arc_distances will be calculated based on the given radius. p will be ignored.

Returns
wKNN

instance; Weights object with binary weights.

See also

libpysal.weights.weights.W

Notes

Ties between neighbors of equal distance are arbitrarily broken.

Examples

Polygon shapefile >>> import libpysal >>> from libpysal.weights import KNN >>> wc=KNN.from_shapefile(libpysal.examples.get_path(“columbus.shp”)) >>> “%.4f”%wc.pct_nonzero ‘4.0816’ >>> set([2,1]) == set(wc.neighbors[0]) True >>> wc3=KNN.from_shapefile(libpysal.examples.get_path(“columbus.shp”),k=3) >>> set(wc3.neighbors[0]) == set([2,1,3]) True >>> set(wc3.neighbors[2]) == set([4,3,0]) True

Point shapefile

>>> w=KNN.from_shapefile(libpysal.examples.get_path("juvenile.shp"))
>>> w.pct_nonzero
1.1904761904761905
>>> w1=KNN.from_shapefile(libpysal.examples.get_path("juvenile.shp"),k=1)
>>> "%.3f"%w1.pct_nonzero
'0.595'
reweight(self, k=None, p=None, new_data=None, new_ids=None, inplace=True)[source]

Redo K-Nearest Neighbor weights construction using given parameters

Parameters
new_datanp.ndarray

an array containing additional data to use in the KNN weight

new_idslist

a list aligned with new_data that provides the ids for each new observation

inplacebool

a flag denoting whether to modify the KNN object in place or to return a new KNN object

kint

number of nearest neighbors

pfloat

Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance Ignored if the KDTree is an ArcKDTree

Returns
A copy of the object using the new parameterization, or None if the
object is reweighted in place.