Add MG python implementation of Leiden

python/cugraph/cugraph/community/leiden.py

@@ -11,7 +11,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from pylibcugraph import louvain as pylibcugraph_leiden
+from pylibcugraph import leiden as pylibcugraph_leiden
 from pylibcugraph import ResourceHandle
 from cugraph.structure import Graph
 import cudf
@@ -31,7 +31,11 @@
 
 
 def leiden(
- G: Union[Graph, "networkx.Graph"], max_iter: int = 100, resolution: float = 1.0
+ G: Union[Graph, "networkx.Graph"],
+ max_iter: int = 100,
+ resolution: float = 1.0,
+ random_state: int = None,
+ theta: int = 1.0,
 ) -> Tuple[cudf.DataFrame, float]:
  """
  Compute the modularity optimizing partition of the input graph using the
@@ -64,6 +68,15 @@ def leiden(
  communities, lower resolutions lead to fewer larger communities.
  Defaults to 1.
 
+ random_state: int, optional(default=None)
+ Random state to use when generating samples. Optional argument,
+ defaults to a hash of process id, time, and hostname.
+
+ theta: float, optional (default=1.0)
+ Called theta in the Leiden algorithm, this is used to scale
+ modularity gain in Leiden refinement phase, to compute
+ the probability of joining a random leiden community.
+
  Returns
  -------
  parts : cudf.DataFrame
@@ -93,9 +106,11 @@ def leiden(
 
  vertex, partition, modularity_score = pylibcugraph_leiden(
  resource_handle=ResourceHandle(),
+ random_state=random_state,
  graph=G._plc_graph,
  max_level=max_iter,
  resolution=resolution,
+ theta=theta,
  do_expensive_check=False,
  )
 

python/cugraph/cugraph/dask/__init__.py

@@ -32,3 +32,4 @@
 from .link_prediction.jaccard import jaccard
 from .link_prediction.sorensen import sorensen
 from .link_prediction.overlap import overlap
+from .community.leiden import leiden

python/cugraph/cugraph/dask/community/__init__.py

@@ -14,3 +14,4 @@
 from .louvain import louvain
 from .triangle_count import triangle_count
 from .induced_subgraph import induced_subgraph
+from .leiden import leiden

python/cugraph/cugraph/dask/community/leiden.py

@@ -0,0 +1,189 @@
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from __future__ import annotations
+
+from dask.distributed import wait, default_client
+import cugraph.dask.comms.comms as Comms
+import dask_cudf
+import dask
+from dask import delayed
+import cudf
+
+from pylibcugraph import ResourceHandle
+from pylibcugraph import leiden as pylibcugraph_leiden
+import numpy
+import cupy as cp
+from typing import Tuple, TYPE_CHECKING
+
+if TYPE_CHECKING:
+ from cugraph import Graph
+
+
+def convert_to_cudf(result: cp.ndarray) -> Tuple[cudf.DataFrame, float]:
+ """
+ Creates a cudf DataFrame from cupy arrays from pylibcugraph wrapper
+ """
+ cupy_vertex, cupy_partition, modularity = result
+ df = cudf.DataFrame()
+ df["vertex"] = cupy_vertex
+ df["partition"] = cupy_partition
+
+ return df, modularity
+
+
+def _call_plc_leiden(
+ sID: bytes,
+ mg_graph_x,
+ max_iter: int,
+ resolution: int,
+ random_state: int,
+ theta: int,
+ do_expensive_check: bool,
+) -> Tuple[cp.ndarray, cp.ndarray, float]:
+ return pylibcugraph_leiden(
+ resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()),
+ random_state=random_state,
+ graph=mg_graph_x,
+ max_level=max_iter,
+ resolution=resolution,
+ theta=theta,
+ do_expensive_check=do_expensive_check,
+ )
+
+
+def leiden(
+ input_graph: Graph,
+ max_iter: int = 100,
+ resolution: int = 1.0,
+ random_state: int = None,
+ theta: int = 1.0,
+) -> Tuple[dask_cudf.DataFrame, float]:
+ """
+ Compute the modularity optimizing partition of the input graph using the
+ Leiden method
+
+ Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden:
+ guaranteeing well-connected communities. Scientific reports, 9(1), 5233.
+ doi: 10.1038/s41598-019-41695-z
+
+ Parameters
+ ----------
+ G : cugraph.Graph
+ The graph descriptor should contain the connectivity information
+ and weights. The adjacency list will be computed if not already
+ present.
+ The current implementation only supports undirected graphs.
+
+ max_iter : integer, optional (default=100)
+ This controls the maximum number of levels/iterations of the Leiden
+ algorithm. When specified the algorithm will terminate after no more
+ than the specified number of iterations. No error occurs when the
+ algorithm terminates early in this manner.
+
+ resolution: float, optional (default=1.0)
+ Called gamma in the modularity formula, this changes the size
+ of the communities. Higher resolutions lead to more smaller
+ communities, lower resolutions lead to fewer larger communities.
+ Defaults to 1.
+
+ random_state: int, optional(default=None)
+ Random state to use when generating samples. Optional argument,
+ defaults to a hash of process id, time, and hostname.
+
+ theta: float, optional (default=1.0)
+ Called theta in the Leiden algorithm, this is used to scale
+ modularity gain in Leiden refinement phase, to compute
+ the probability of joining a random leiden community.
+
+ Returns
+ -------
+ parts : dask_cudf.DataFrame
+ GPU data frame of size V containing two columns the vertex id and the
+ partition id it is assigned to.
+
+ ddf['vertex'] : cudf.Series
+ Contains the vertex identifiers
+ ddf['partition'] : cudf.Series
+ Contains the partition assigned to the vertices
+
+ modularity_score : float
+ a floating point number containing the global modularity score of the
+ partitioning.
+
+ Examples
+ --------
+ >>> from cugraph.experimental.datasets import karate
+ >>> G = karate.get_graph(fetch=True)
+ >>> parts, modularity_score = cugraph.leiden(G)
+
+ """
+
+ if input_graph.is_directed():
+ raise ValueError("input graph must be undirected")
+
+ # Return a client if one has started
+ client = default_client()
+
+ do_expensive_check = False
+
+ result = [
+ client.submit(
+ _call_plc_leiden,
+ Comms.get_session_id(),
+ input_graph._plc_graph[w],
+ max_iter,
+ resolution,
+ random_state,
+ theta,
+ do_expensive_check,
+ workers=[w],
+ allow_other_workers=False,
+ )
+ for w in Comms.get_workers()
+ ]
+
+ wait(result)
+
+ part_mod_score = [client.submit(convert_to_cudf, r) for r in result]
+ wait(part_mod_score)
+
+ vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0]
+ empty_df = cudf.DataFrame(
+ {
+ "vertex": numpy.empty(shape=0, dtype=vertex_dtype),
+ "partition": numpy.empty(shape=0, dtype="int32"),
+ }
+ )
+
+ part_mod_score = [delayed(lambda x: x, nout=2)(r) for r in part_mod_score]
+
+ ddf = dask_cudf.from_delayed(
+ [r[0] for r in part_mod_score], meta=empty_df, verify_meta=False
+ ).persist()
+
+ mod_score = dask.array.from_delayed(
+ part_mod_score[0][1], shape=(1,), dtype=float
+ ).compute()
+
+ wait(ddf)
+ wait(mod_score)
+
+ wait([r.release() for r in part_mod_score])
+
+ if input_graph.renumbered:
+ ddf = input_graph.unrenumber(ddf, "vertex")
+
+ return ddf, mod_score

python/cugraph/cugraph/dask/community/louvain.py

@@ -13,28 +13,40 @@
 # limitations under the License.
 #
 
+from __future__ import annotations
+
 from dask.distributed import wait, default_client
 import cugraph.dask.comms.comms as Comms
 import dask_cudf
+import dask
+from dask import delayed
 import cudf
-import operator as op
+import cupy as cp
+import numpy
 
 from pylibcugraph import ResourceHandle
 from pylibcugraph import louvain as pylibcugraph_louvain
+from typing import Tuple, TYPE_CHECKING
+
+if TYPE_CHECKING:
+ from cugraph import Graph
 
 
-def convert_to_cudf(cupy_vertex, cupy_partition):
+def convert_to_cudf(result: cp.ndarray) -> Tuple[cudf.DataFrame, float]:
  """
  Creates a cudf DataFrame from cupy arrays from pylibcugraph wrapper
  """
+ cupy_vertex, cupy_partition, modularity = result
  df = cudf.DataFrame()
  df["vertex"] = cupy_vertex
  df["partition"] = cupy_partition
 
- return df
+ return df, modularity
 
 
-def _call_plc_louvain(sID, mg_graph_x, max_iter, resolution, do_expensive_check):
+def _call_plc_louvain(
+ sID: bytes, mg_graph_x, max_iter: int, resolution: int, do_expensive_check: bool
+) -> Tuple[cp.ndarray, cp.ndarray, float]:
  return pylibcugraph_louvain(
  resource_handle=ResourceHandle(Comms.get_handle(sID).getHandle()),
  graph=mg_graph_x,
@@ -44,7 +56,9 @@ def _call_plc_louvain(sID, mg_graph_x, max_iter, resolution, do_expensive_check)
  )
 
 
-def louvain(input_graph, max_iter=100, resolution=1.0):
+def louvain(
+ input_graph: Graph, max_iter: int = 100, resolution: int = 1.0
+) -> Tuple[dask_cudf.DataFrame, float]:
  """
  Compute the modularity optimizing partition of the input graph using the
  Louvain method
@@ -122,32 +136,31 @@ def louvain(input_graph, max_iter=100, resolution=1.0):
 
  wait(result)
 
- # futures is a list of Futures containing tuples of (DataFrame, mod_score),
- # unpack using separate calls to client.submit with a callable to get
- # individual items.
- # FIXME: look into an alternate way (not returning a tuples, accessing
- # tuples differently, etc.) since multiple client.submit() calls may not be
- # optimal.
- result_vertex = [client.submit(op.getitem, f, 0) for f in result]
- result_partition = [client.submit(op.getitem, f, 1) for f in result]
- mod_score = [client.submit(op.getitem, f, 2) for f in result]
-
- cudf_result = [
-  client.submit(convert_to_cudf, cp_vertex_arrays, cp_partition_arrays)
- for cp_vertex_arrays, cp_partition_arrays in zip(
-  result_vertex, result_partition
- )
- ]
+ part_mod_score = [client.submit(convert_to_cudf, r) for r in result]
+ wait(part_mod_score)
+
+ vertex_dtype = input_graph.edgelist.edgelist_df.dtypes[0]
+ empty_df = cudf.DataFrame(
+  {
+  "vertex": numpy.empty(shape=0, dtype=vertex_dtype),
+  "partition": numpy.empty(shape=0, dtype="int32"),
+  }
+ )
+
+ part_mod_score = [delayed(lambda x: x, nout=2)(r) for r in part_mod_score]
+
+ ddf = dask_cudf.from_delayed(
+ [r[0] for r in part_mod_score], meta=empty_df, verify_meta=False
+ ).persist()
 
- wait(cudf_result)
- # Each worker should have computed the same mod_score
- mod_score = mod_score[0].result()
+ mod_score = dask.array.from_delayed(
+  part_mod_score[0][1], shape=(1,), dtype=float
+ ).compute()
 
- ddf = dask_cudf.from_delayed(cudf_result).persist()
  wait(ddf)
+ wait(mod_score)
 
- # Wait until the inactive futures are released
- wait([(r.release(), c_r.release()) for r, c_r in zip(result, cudf_result)])
+ wait([r.release() for r in part_mod_score])
 
  if input_graph.renumbered:
  ddf = input_graph.unrenumber(ddf, "vertex")