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lint graph/base.py #615

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Oct 31, 2023
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lint graph/base.py #615

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531be29
lint graph/base.py
jGaboardi Oct 31, 2023
7e00e19
Apply suggestions from code review
jGaboardi Oct 31, 2023
9c94e76
Update libpysal/graph/base.py
jGaboardi Oct 31, 2023
25001c5
fix strict keyword
jGaboardi Oct 31, 2023
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130 changes: 64 additions & 66 deletions libpysal/graph/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,8 @@
class Graph(_Set_Mixin):
"""Graph class encoding spatial weights matrices

The :class:`Graph` is currently experimental and its API is incomplete and unstable.
The :class:`Graph` is currently experimental
and its API is incomplete and unstable.
"""

def __init__(self, adjacency, transformation="O"):
Expand All @@ -45,8 +46,8 @@
It is recommenced to use one of the ``from_*`` or ``build_*`` constructors
rather than invoking ``__init__`` directly.

Each observation needs to be present in the focal, at least as a self-loop with
a weight 0.
Each observation needs to be present in the focal,
at least as a self-loop with a weight 0.

Parameters
----------
Expand All @@ -63,7 +64,6 @@
- **D** -- Double-standardization (global sum :math:`=1`)
- **V** -- Variance stabilizing


"""
if not isinstance(adjacency, pd.Series):
raise TypeError(
Expand Down Expand Up @@ -147,7 +147,7 @@
return self._adjacency.copy()

@classmethod
def from_W(cls, w):
def from_W(cls, w): # noqa N802
"""Create an experimental Graph from libpysal.weights.W object

Parameters
Expand All @@ -161,7 +161,7 @@
"""
return cls.from_weights_dict(dict(w))

def to_W(self):
def to_W(self): # noqa N802
"""Convert Graph to a libpysal.weights.W object

Returns
Expand Down Expand Up @@ -226,9 +226,10 @@
zip(
[focal_col, neighbor_col, weight_col],
["focal_col", "neighbor_col", "weight_col"],
strict=True,
)
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)
for col in cols.keys():
for col in cols:
assert col in adjacency.columns.tolist(), (
f'"{col}" was given for `{cols[col]}`, but the '
f"columns available in `adjacency` are: {adjacency.columns.tolist()}."
Expand Down Expand Up @@ -307,10 +308,8 @@
Graph
libpysal.graph.Graph based on weights dictionary of dictionaries
"""
idx = {f: [k for k in neighbors] for f, neighbors in weights_dict.items()}
data = {
f: [k for k in neighbors.values()] for f, neighbors in weights_dict.items()
}
idx = {f: list(neighbors) for f, neighbors in weights_dict.items()}
data = {f: list(neighbors.values()) for f, neighbors in weights_dict.items()}
return cls.from_dicts(idx, data)

@classmethod
Expand Down Expand Up @@ -339,9 +338,9 @@
"""Generate Graph from geometry based on contiguity

Contiguity builder assumes that all geometries are forming a coverage, i.e.
a non-overlapping mesh and neighbouring geometries share only points or segments
of their exterior boundaries. In practice, ``build_contiguity`` is capable of
creating a Graph of partially overlapping geometries when
a non-overlapping mesh and neighbouring geometries share only points or
segments of their exterior boundaries. In practice, ``build_contiguity`` is
capable of creating a Graph of partially overlapping geometries when
``strict=False, by_perimeter=False``, but that would not strictly follow the
definition of queen or rook contiguity.

Expand All @@ -352,22 +351,22 @@
resulting Graph is indexed by the original index. If an array of
shapely.Geometry objects is passed, Graph will assume a RangeIndex.
rook : bool, optional
Contiguity method. If True, two geometries are considered neighbours if they
share at least one edge. If False, two geometries are considered neighbours
if they share at least one vertex. By default True
Contiguity method. If True, two geometries are considered neighbours if
they share at least one edge. If False, two geometries are considered
neighbours if they share at least one vertex. By default True
by_perimeter : bool, optional
If True, ``weight`` represents the length of the shared boundary between
adjacent units, by default False. For row-standardized version of perimeter
weights, use
``Graph.build_contiguity(gdf, by_perimeter=True).transform("r")``.
strict : bool, optional
Use the strict topological method. If False, the contiguity is determined
based on shared coordinates or coordinate sequences representing edges. This
assumes geometry coverage that is topologically correct. This method is
faster but can miss some relations. If True, the contiguity is determined
based on geometric relations that do not require precise topology. This
method is slower but will result in correct contiguity even if the topology
of geometries is not optimal. By default False
based on shared coordinates or coordinate sequences representing edges.
This assumes geometry coverage that is topologically correct. This method
is faster but can miss some relations. If True, the contiguity is
determined based on geometric relations that do not require precise
topology. This method is slower but will result in correct contiguity
even if the topology of geometries is not optimal. By default False.

Returns
-------
Expand Down Expand Up @@ -448,12 +447,11 @@
p : int (default: 2)
parameter for minkowski metric, ignored if metric != "minkowski".
coincident: str, optional (default "raise")
Method for handling coincident points when ``k`` is not None. Options include
Method for handling coincident points when ``k`` is not None. Options are
``'raise'`` (raising an exception when coincident points are present),
``'jitter'`` (randomly displace coincident points to produce uniqueness), and
``'jitter'`` (randomly displace coincident points to produce uniqueness), &
``'clique'`` (induce fully-connected sub cliques for coincident points).


Returns
-------
Graph
Expand Down Expand Up @@ -498,7 +496,7 @@
coincident: str, optional (default "raise")
Method for handling coincident points. Options include
``'raise'`` (raising an exception when coincident points are present),
``'jitter'`` (randomly displace coincident points to produce uniqueness), and
``'jitter'`` (randomly displace coincident points to produce uniqueness), &
``'clique'`` (induce fully-connected sub cliques for coincident points).


Expand Down Expand Up @@ -583,7 +581,7 @@
coincident: str, optional (default "raise")
Method for handling coincident points. Options include
``'raise'`` (raising an exception when coincident points are present),
``'jitter'`` (randomly displace coincident points to produce uniqueness), and
``'jitter'`` (randomly displace coincident points to produce uniqueness), &
``'clique'`` (induce fully-connected sub cliques for coincident points).

Returns
Expand Down Expand Up @@ -737,15 +735,16 @@
):
"""Generate Graph from fuzzy contiguity

Fuzzy contiguity relaxes the notion of contiguity neighbors for the case of
geometry collections that violate the condition of planar enforcement. It
handles three types of conditions present in such collections that would result
in missing links when using the regular contiguity methods.
Fuzzy contiguity relaxes the notion of contiguity neighbors
for the case of geometry collections that violate the condition
of planar enforcement. It handles three types of conditions present
in such collections that would result in missing links when using
the regular contiguity methods.

The first are edges for nearby polygons that should be shared, but are digitized
separately for the individual polygons and the resulting edges do not
coincide, but instead the edges intersect. This case can also be covered by
``build_contiguty`` with the ``strict=False`` parameter.
The first are edges for nearby polygons that should be shared, but are
digitized separately for the individual polygons and the resulting edges
do not coincide, but instead the edges intersect. This case can also be
covered by ``build_contiguty`` with the ``strict=False`` parameter.

The second case is similar to the first, only the resultant edges do not
intersect but are "close". The optional buffering of geometry then closes the
Expand All @@ -772,10 +771,10 @@
``tolerance`` or ``buffer`` may be specified but not both.
By default None.
predicate : str, optional
The predicate to use for determination of neighbors. Default is 'intersects'.
If None is passed, neighbours are determined based on the intersection of
bounding boxes. See the documentation of ``geopandas.GeoSeries.sindex.query``
for allowed predicates.
The predicate to use for determination of neighbors. Default is
'intersects'. If None is passed, neighbours are determined based
on the intersection of bounding boxes. See the documentation of
``geopandas.GeoSeries.sindex.query`` for allowed predicates.

Returns
-------
Expand Down Expand Up @@ -904,8 +903,8 @@
s = self._adjacency.groupby(level=0).transform(
lambda group: group / math.sqrt((group**2).sum())
)
nQ = self.n / s.sum()
standardized = (s * nQ).fillna(0).values # isolate comes as NaN -> 0
n_q = self.n / s.sum()
standardized = (s * n_q).fillna(0).values # isolate comes as NaN -> 0

else:
raise ValueError(
Expand Down Expand Up @@ -965,7 +964,8 @@
def isolates(self):
"""Index of observations with no neighbors

Isolates are encoded as a self-loop with the weight == 0 in the adjacency table.
Isolates are encoded as a self-loop with
the weight == 0 in the adjacency table.

Returns
-------
Expand Down Expand Up @@ -1012,7 +1012,6 @@

Parameters
----------

intrinsic : bool, optional
Default is ``True``. Intrinsic symmetry is defined as:

Expand All @@ -1033,13 +1032,11 @@

Returns
-------

pandas.Series
A ``Series`` of ``(i,j)`` pairs of asymmetries sorted
ascending by the focal observation (index value),
where ``i`` is the focal and ``j`` is the neighbor.
An empty ``Series`` is returned if no asymmetries are found.

"""
if intrinsic:
wd = self.sparse.transpose() - self.sparse
Expand All @@ -1055,7 +1052,9 @@
dtype=self._adjacency.index.dtypes["focal"],
)
else:
i2id = dict(zip(np.arange(self.unique_ids.shape[0]), self.unique_ids))
i2id = dict(
zip(np.arange(self.unique_ids.shape[0]), self.unique_ids, strict=True)
)
focal, neighbor = np.nonzero(wd)
focal = focal.astype(self._adjacency.index.dtypes["focal"])
neighbor = neighbor.astype(self._adjacency.index.dtypes["focal"])
Expand All @@ -1070,9 +1069,9 @@
def higher_order(self, k=2, shortest_path=True, diagonal=False, lower_order=False):
"""Contiguity weights object of order :math:`k`.

Proper higher order neighbors are returned such that :math:`i` and :math:`j` are
:math:`k`-order neighbors if the shortest path from :math:`i-j` is of length
:math:`k`.
Proper higher order neighbors are returned such that :math:`i` and :math:`j`
are :math:`k`-order neighbors if the shortest path from :math:`i-j` is of
length :math:`k`.

Parameters
----------
Expand Down Expand Up @@ -1102,22 +1101,22 @@
sp = sparse.csr_matrix(binary.sparse)

if lower_order:
wk = sum(map(lambda x: sp**x, range(2, k + 1)))
wk = sum(sp**x for x in range(2, k + 1))
shortest_path = False
else:
wk = sp**k

rk, ck = wk.nonzero()
sk = set(zip(rk, ck))
sk = set(zip(rk, ck, strict=True))

if shortest_path:
for j in range(1, k):
wj = sp**j
rj, cj = wj.nonzero()
sj = set(zip(rj, cj))
sj = set(zip(rj, cj, strict=True))
sk.difference_update(sj)
if not diagonal:
sk = set([(i, j) for i, j in sk if i != j])
sk = {(i, j) for i, j in sk if i != j}

return Graph.from_sparse(
sparse.coo_array(
Expand All @@ -1133,9 +1132,9 @@
def lag(self, y):
"""Spatial lag operator

If weights are row standardized, returns the mean of each observation's neighbors;
if not, returns the weighted sum of each observation's neighbors.

If weights are row standardized, returns the mean of each
observation's neighbors; if not, returns the weighted sum
of each observation's neighbors.

Parameters
----------
Expand Down Expand Up @@ -1183,12 +1182,9 @@
try:
import networkx as nx
except ImportError:
raise ImportError("NetworkX is required.")
raise ImportError("NetworkX is required.") from None

if self.asymmetry().empty:
graph_type = nx.Graph
else:
graph_type = nx.DiGraph
graph_type = nx.Graph if self.asymmetry().empty else nx.DiGraph

return nx.from_pandas_edgelist(
self._adjacency.reset_index(),
Expand Down Expand Up @@ -1294,7 +1290,9 @@
if ids is None:
ids = np.unique(np.hstack((heads, tails)))
lookup = list(ids).index
input_df = pd.DataFrame.from_dict(dict(focal=heads, neighbor=tails, weight=weights))
input_df = pd.DataFrame.from_dict(

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{"focal": heads, "neighbor": tails, "weight": weights}
)
return (
input_df.set_index(["focal", "neighbor"])
.assign(
Expand All @@ -1311,9 +1309,9 @@
def read_parquet(path, **kwargs):
"""Read Graph from a Apache Parquet

Read Graph serialized using `Graph.to_parquet()` back into the `Graph` object. The
Parquet file needs to contain adjacency table with a structure required by the `Graph`
constructor and optional metadata with the type of transformation.
Read Graph serialized using `Graph.to_parquet()` back into the `Graph` object.
The Parquet file needs to contain adjacency table with a structure required
by the `Graph` constructor and optional metadata with the type of transformation.

Parameters
----------
Expand Down
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