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
 kdtree
object
PySAL KDTree or ArcKDTree where KDtree.data is array (n,k) n observations on k characteristics used to measure distances between the n objects
 k
int
number of nearest neighbors
 p
float
Minkowski pnorm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance Ignored if the KDTree is an ArcKDTree
 ids
list
identifiers to attach to each observation
 kdtree
 Returns
 w
W
instance Weights object with binary weights
 w
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 PySALW
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 fromw.id_order
tonew_ids
in all.reweight
(self[, k, p, new_data, new_ids, …])Redo KNearest 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 setid_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 inid_order
.nonzero
Number of nonzero weights.
pct_nonzero
Percentage of nonzero weights.
s0
s0
is defined ass1
s1
is defined ass2
s2
is defined ass2array
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
 array
np.ndarray
(n, k) array representing n observations on k characteristics used to measure distances between the n objects
 **kwargs
keyword
arguments
,see
Rook
 array
 Returns
 w
W
instance Weights object with binary weights
 w
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
See also
libpysal.weights.weights.W

classmethod
from_shapefile
(filepath, *args, **kwargs)[source]¶ Nearest neighbor weights from a shapefile.
 Parameters
 data
str
shapefile containing attribute data.
 k
int
number of nearest neighbors
 p
float
Minkowski pnorm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance
 ids
list
identifiers to attach to each observation
 radius
float
If supplied arc_distances will be calculated based on the given radius. p will be ignored.
 data
 Returns
 w
KNN
instance; Weights object with binary weights.
 w
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 KNearest Neighbor weights construction using given parameters
 Parameters
 new_data
np.ndarray
an array containing additional data to use in the KNN weight
 new_ids
list
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
 k
int
number of nearest neighbors
 p
float
Minkowski pnorm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance Ignored if the KDTree is an ArcKDTree
 new_data
 Returns