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subg.py
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"""
Subgraph transforms for visualization, graph algorithms, etc.
see license https://github.com/DerwenAI/kglab#license-and-copyright
"""
import typing
from icecream import ic # type: ignore # pylint: disable=W0611
from tqdm import tqdm # type: ignore
import pandas as pd # type: ignore
import pyvis.network # type: ignore
import networkx as nx # type: ignore
import numpy as np # type: ignore
from .kglab import KnowledgeGraph
from .topo import Measure
from .pkg_types import NodeLike, RDF_Node, RDF_Triple
from .algebra import AlgebraMixin
from .networks import NetAnalysisMixin
from .util import get_gpu_count
if get_gpu_count() > 0:
import cudf # type: ignore
import cugraph # type: ignore # pylint: disable=W0611
class Subgraph:
"""
Base class for projection of an RDF graph into an *algebraic object* such as a *vector*,
*matrix*, or *tensor* representation, to support integration with non-RDF graph libraries.
In other words, this class provides means to vectorize selected portions of a graph as a
[*dimension*](https://mathworld.wolfram.com/Dimension.html).
See <https://derwen.ai/docs/kgl/concepts/#subgraph>
Features support several areas of use cases, including:
* label encoding
* vectorization (parallel processing)
* graph algorithms
* visualization
* embedding (deep learning)
* probabilistic graph inference (statistical relational learning)
The base case is where a *subset* of the nodes in the source RDF graph get represented as
a *vector*, in the `node_vector` member. This provides an efficient *index* on a constructed
*dimension*, solely for the context of a specific use case.
"""
kg: KnowledgeGraph
nx_graph: typing.Optional[nx.DiGraph] = None
def __init__ (
self,
kg: KnowledgeGraph,
*,
preload: list = None,
) -> None:
"""
Constructor for creating and manipulating a *subgraph* as a [*vector*](https://mathworld.wolfram.com/Vector.html),
projecting from an RDF graph represented by a `KnowledgeGraph` object.
kg:
the source RDF graph
preload:
an optional, pre-determined list to pre-load for *label encoding*
"""
self.kg = kg
if preload:
self.node_vector = preload
else:
self.node_vector = []
def transform (
self,
node: NodeLike,
) -> int:
"""
Transforms a node in an RDF graph to an integer value, as a unique identifier with the closure of a specific use case.
The integer value can then be used to index into an *algebraic object* such as a *matrix* or *tensor*.
Effectvely, this method is similar to a [`sklearn.preprocessing.LabelEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html).
Notes:
* the integer value is **not** a [*uuid*](https://tools.ietf.org/html/rfc4122) since it is only defined within the closure of a specific use case.
* a special value `-1` represents the unique identifier for a non-existent (`None`) node, which is useful in data structures that have optional placeholders for links to RDF nodes
node:
a node in the RDF graph
returns:
a unique identifier (an integer index) for the `node` in the RDF graph
"""
# NB: workaround to avoid Python calling `node.__nonzero__()`
# https://amir.rachum.com/blog/2012/08/25/you-cant-handle-the-truth/
# otherwise, if `node` is a literal (e.g., an int, float, string,
# duration, etc.) that evals to any zero value then this function
# would behave incorrectly
if node is None:
# null case
return -1
if not node in self.node_vector:
self.node_vector.append(node)
return self.node_vector.index(node)
def inverse_transform (
self,
id: int,
) -> NodeLike:
"""
Inverse transform from an integer to a node in the RDF graph, using the identifier as an index into the node vector.
id:
an integer index for the `node` in the RDF graph
returns:
node in the RDF graph
"""
if id < 0:
return None
return self.node_vector[id]
def n3fy (
self,
node: RDF_Node,
) -> typing.Any:
"""
Wrapper for RDFlib [`n3()`](https://rdflib.readthedocs.io/en/stable/utilities.html?highlight=n3#serializing-a-single-term-to-n3) and [`toPython()`](https://rdflib.readthedocs.io/en/stable/apidocs/rdflib.html?highlight=toPython#rdflib.Variable.toPython) to serialize a node into a human-readable representation using N3 format.
This method provides a convenience, which in turn calls `KnowledgeGraph.n3fy()`
node:
must be a [`rdflib.term.Node`](https://rdflib.readthedocs.io/en/stable/apidocs/rdflib.html?highlight=Node#rdflib.term.Node)
returns:
text (or Python object) for the serialized node
"""
return self.kg.n3fy(node)
def check_attributes(self):
"""
Check if needed attributes are set.
returns:
None
"""
if self.kg is None:
raise ValueError(
"""`Subgraph`'s `kg` should be initialized:
`kglab.Subgraph(kg)`"""
)
# create an empty `nx.DiGraph` if none is present
if self.nx_graph is None:
# NOTE: find a way to pass `bipartite` if needed
self.nx_graph = self.build_nx_graph(nx.DiGraph()) # pylint: disable=E1101
class SubgraphMatrix (Subgraph, AlgebraMixin, NetAnalysisMixin): # pylint: disable=W0223
"""
Projection of a RDF graph to a [*matrix*](https://mathworld.wolfram.com/AdjacencyMatrix.html) representation.
Typical use cases include integration with non-RDF graph libraries for *graph algorithms*.
SPARQL query text needs to define a subgraph as: `subject -> object`.
"""
_SRC_DST_MAP: typing.List[str] = ["subject", "object"]
def __init__ (
self,
kg: KnowledgeGraph,
sparql: str,
*,
bindings: dict = None,
src_dst: typing.List[str] = None,
) -> None:
"""
Constructor for creating and manipulating a *subgraph* as a [*matrix*](https://mathworld.wolfram.com/AdjacencyMatrix.html),
projecting from an RDF graph represented by a `KnowledgeGraph` object.
kg:
the source RDF graph
sparql:
text for a SPARQL query that yields pairs to project into the *subgraph*; by default this expects the query to return
bindings for `subject` and `object` nodes in the RDF graph
bindings:
initial variable bindings
src_dst:
an optional map to override the `subject` and `object` bindings expected in the SPARQL query results; defaults to `None`
"""
super().__init__(kg=kg)
self.sparql = sparql
self.bindings:typing.Optional[dict] = bindings
if src_dst:
self.src_dst: typing.List[str] = src_dst
else:
self.src_dst = self._SRC_DST_MAP
def build_df (
self,
*,
show_symbols: bool = False,
) -> pd.DataFrame:
"""
Factory pattern to populate a [`pandas.DataFrame`](https://pandas.pydata.org/docs/reference/frame.html) object,
using transforms in this subgraph.
Note: this method is primarily intended for [`cuGraph`](https://docs.rapids.ai/api/cugraph/stable/) support.
Loading via a `DataFrame` is required in lieu of using the `nx.add_node()` approach.
Therefore the support for representing *bipartite* graphs is still pending.
show_symbols:
optionally, include the symbolic representation for each node; defaults to `False`
returns:
the populated `DataFrame` object; uses the [RAPIDS `cuDF` library](https://docs.rapids.ai/api/cudf/stable/) if GPUs are enabled
"""
col_names: typing.List[str] = [ "src", "dst", "src_sym", "dst_sym" ]
if self.sparql is None and self.kg.use_gpus is True:
raise ValueError("""To use GPUs is necessary to provide a SPARQL query to define a subgraph:
`kglab.SubgraphMatrix(kg, sparql)` or `SubgraphTensor(...)`""")
row_iter = self.kg.query(self.sparql, bindings=self.bindings)
if not show_symbols:
col_names = col_names[:2]
rows_list: typing.List[dict] = [
{
col_names[0]: self.transform(row[self.src_dst[0]]),
col_names[1]: self.transform(row[self.src_dst[1]]),
}
for row in row_iter
]
else:
rows_list = [
{
col_names[0]: self.transform(row[self.src_dst[0]]),
col_names[1]: self.transform(row[self.src_dst[1]]),
col_names[2]: self.n3fy(row[self.src_dst[0]]),
col_names[3]: self.n3fy(row[self.src_dst[1]]),
}
for row in row_iter
]
if self.kg.use_gpus is True:
df = cudf.DataFrame(rows_list, columns=col_names)
else:
df = pd.DataFrame(rows_list, columns=col_names)
return df
def build_nx_graph (
self,
nx_graph: nx.DiGraph,
*,
bipartite: bool = False,
) -> nx.DiGraph:
"""
Factory pattern to populate a [`networkx.DiGraph`](https://networkx.org/documentation/latest/reference/classes/digraph.html) object, using transforms in this subgraph.
See <https://networkx.org/>
nx_graph:
pass in an unpopulated [`networkx.DiGraph`](https://networkx.org/documentation/latest/reference/classes/digraph.html) object; must be a [`cugraph.DiGraph`](https://docs.rapids.ai/api/cugraph/stable/api.html#digraph) if GPUs are enabled
bipartite:
flag for whether the `(subject, object)` pairs should be partitioned into *bipartite sets*, in other words whether the *adjacency matrix* is symmetric; ignored if GPUs are enabled
returns:
the populated `NetworkX` graph object; uses the [RAPIDS `cuGraph` library](https://docs.rapids.ai/api/cugraph/stable/) if GPUs are enabled
"""
if self.kg.use_gpus is True:
df = self.build_df()
nx_graph.from_cudf_edgelist(df, source="src", destination="dst")
else:
for row in self.kg.query(self.sparql, bindings=self.bindings):
s_id = self.transform(row[self.src_dst[0]])
s_label = self.n3fy(row[self.src_dst[0]])
o_id = self.transform(row[self.src_dst[1]])
o_label = self.n3fy(row[self.src_dst[1]])
if bipartite:
nx_graph.add_node(s_id, label=s_label, bipartite=0)
nx_graph.add_node(o_id, label=o_label, bipartite=1)
else:
nx_graph.add_node(s_id, label=s_label)
nx_graph.add_node(o_id, label=o_label)
nx_graph.add_edge(s_id, o_id)
return nx_graph
def build_ig_graph (
self,
ig_graph: typing.Any,
) -> typing.Any:
"""
Factory pattern to populate an [`igraph.Graph`](https://igraph.org/python/doc/igraph.Graph-class.html) object, using transforms in this subgraph.
See <https://igraph.org/python/doc/>
Note that `iGraph` is somewhat notorious for being quite difficult to install correctly across a wide range of different platforms and environments.
Consequently this has been removed from being a dependency for `kglab`; to use `iGraph` please install and import it separately.
ig_graph:
pass in an unpopulated [`igraph.Graph`](https://igraph.org/python/doc/igraph.Graph-class.html) object
returns:
the populated `iGraph` graph object
"""
measure = Measure()
measure.measure_graph(self.kg)
keyset = measure.get_keyset(incl_pred=False)
ig_graph.add_vertices(n=keyset)
for row in self.kg.query(self.sparql, bindings=self.bindings):
s_id = self.transform(row[self.src_dst[0]])
o_id = self.transform(row[self.src_dst[1]])
ig_graph.add_edges([ (s_id, o_id,) ])
ig_graph.vs["label"] = ig_graph.vs["name"] # pylint: disable=E1136,E1137
return ig_graph
def _get_n_nodes(self):
""" Return number of nodes counted from the adjancency matrix"""
return self.to_adjacency().shape[0]
def _get_n_edges(self):
""" Return number of edges counted from the adjancency matrix"""
return int(np.sum(self.to_adjacency()))
class SubgraphTensor (Subgraph):
"""
Projection of a RDF graph to a [*tensor*](https://mathworld.wolfram.com/Tensor.html) representation.
Typical use cases include integration with non-RDF graph libraries for *visualization* and *embedding*.
"""
def __init__ (
self,
kg: KnowledgeGraph,
*,
excludes: list = None,
) -> None:
"""
Constructor for creating and manipulating a *subgraph* as a [*tensor*](https://mathworld.wolfram.com/Tensor.html),
projecting from an RDF graph represented by a `KnowledgeGraph` object.
kg:
the source RDF graph
excludes:
a list of RDF predicates to exclude from projection into the *subgraph*
"""
super().__init__(kg=kg)
if excludes:
self.excludes = excludes
else:
self.excludes = []
def as_tuples (
self
) -> typing.Generator[RDF_Triple, None, None]:
"""
Iterator for enumerating the RDF triples to be included in the subgraph, used in factory patterns for visualizations.
This allows a kind of *lazy evaluation*.
yields:
the RDF triples within the subgraph
"""
for s, p, o in self.kg.rdf_graph():
if not p in self.excludes:
yield s, p, o
def as_tensor_edges (
self
) -> typing.Generator[typing.List[int], None, None]:
"""
Iterator for enumerating the edges connecting to each predicate in the
subgraph, to be used to represent the KG in `PyTorch`.
yields:
a subject and object edge for each predicate, in tensor representation
"""
for s, p, o in self.as_tuples():
s_label = self.n3fy(s)
s_id = self.transform(s_label)
p_label = self.n3fy(p)
p_id = self.transform(p_label)
o_label = self.n3fy(o)
o_id = self.transform(o_label)
yield [s_id, o_id, 2 * p_id]
yield [o_id, s_id, 2 * p_id + 1]
def as_tensor (
self,
*,
quiet: bool = True,
) -> typing.List[typing.Tuple[int, int, int]]:
"""
Represents the KG as an edge list where each predicate has edges
connecting to its subject and object.
This can be used to load a [`Tensor`](https://pytorch.org/docs/stable/tensors.html)
in PyTorch, for example:
```
edge_list = kg.as_tensor()
tensor = torch.tensor(edge_list, dtype=torch.long).t().contiguous()
```
quiet:
boolean flag to disable `tqdm` progress bar calculation and output
returns:
an edge list for the loaded tensor object
"""
edge_list: list = list(
edge_tuple
for edge_tuple in tqdm(self.as_tensor_edges(), disable=quiet) # pylint: disable=R1721
)
return edge_list
######################################################################
## visualization
##
## Automated Network Graph: The triples describing relationships
## between entities can be ingested into graph visualization tools
## to extend or create an analyst's account-specific network
## model.
def pyvis_style_node ( # pylint: disable=R0201
self,
pyvis_graph: pyvis.network.Network,
node_id: int,
label: str,
*,
style: dict = None,
) -> None : # pylint: disable=R0201
"""
Adds a node into a [PyVis](https://pyvis.readthedocs.io/) network, optionally with styling info.
pyvis_graph:
the [`pyvis.network.Network`](https://pyvis.readthedocs.io/en/latest/documentation.html?highlight=network#pyvis.network.Network) being used for *interactive visualization*
node_id:
unique identifier for a node in the RDF graph
label:
text label for the node
style:
optional style dictionary
"""
if not style:
style = {}
prefix = label.split(":")[0]
if prefix in style:
pyvis_graph.add_node(
node_id,
label=label,
title=label,
color=style[prefix]["color"],
size=style[prefix]["size"],
)
else:
pyvis_graph.add_node(
node_id,
label=label,
title=label,
)
def build_pyvis_graph (
self,
*,
notebook: bool = False,
style: dict = None,
) -> pyvis.network.Network:
"""
Factory pattern to create a [`pyvis.network.Network`](https://pyvis.readthedocs.io/en/latest/documentation.html?highlight=network#pyvis.network.Network) object, populated by transforms in this subgraph.
See <https://pyvis.readthedocs.io/>
notebook:
flag for whether or not the interactive visualization will be generated within a notebook
style:
optional style dictionary
returns:
a `PyVis` network object
"""
pyvis_graph = pyvis.network.Network(notebook=notebook)
if not style:
style = {}
for s, p, o in self.as_tuples():
# label the subject
s_label = self.n3fy(s)
s_id = self.transform(s_label)
self.pyvis_style_node(pyvis_graph, s_id, s_label, style=style)
# label the object
o_label = str(self.n3fy(o))
o_id = self.transform(o_label)
self.pyvis_style_node(pyvis_graph, o_id, o_label, style=style)
# label the predicate
p_label = self.n3fy(p)
pyvis_graph.add_edge(s_id, o_id, label=p_label)
return pyvis_graph