libpysal.weights.KNN¶
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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
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__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])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 array.
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 meta data 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_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 \(W^{'}W + WW\).
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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
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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_colstring
column name of the geometry stored in df
- idsstring 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
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classmethod
from_shapefile
(filepath, *args, **kwargs)[source]¶ Nearest neighbor weights from a shapefile.
- Parameters
- datastring
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'
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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.