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Add MG python implementation of Leiden (#3566)
This PR adds an MG implementation of Leiden following the PLC path closes #2489 closes #2490 closes #3431 Authors: - Joseph Nke (https://github.com/jnke2016) - Naim (https://github.com/naimnv) - Chuck Hastings (https://github.com/ChuckHastings) - Brad Rees (https://github.com/BradReesWork) Approvers: - Chuck Hastings (https://github.com/ChuckHastings) - Rick Ratzel (https://github.com/rlratzel) URL: #3566
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# 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. | ||
# | ||
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from __future__ import annotations | ||
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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 | ||
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from pylibcugraph import ResourceHandle | ||
from pylibcugraph import leiden as pylibcugraph_leiden | ||
import numpy | ||
import cupy as cp | ||
from typing import Tuple, TYPE_CHECKING | ||
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if TYPE_CHECKING: | ||
from cugraph import Graph | ||
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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 | ||
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return df, modularity | ||
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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, | ||
) | ||
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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) | ||
""" | ||
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if input_graph.is_directed(): | ||
raise ValueError("input graph must be undirected") | ||
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# Return a client if one has started | ||
client = default_client() | ||
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do_expensive_check = False | ||
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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() | ||
] | ||
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wait(result) | ||
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part_mod_score = [client.submit(convert_to_cudf, r) for r in result] | ||
wait(part_mod_score) | ||
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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"), | ||
} | ||
) | ||
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part_mod_score = [delayed(lambda x: x, nout=2)(r) for r in part_mod_score] | ||
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ddf = dask_cudf.from_delayed( | ||
[r[0] for r in part_mod_score], meta=empty_df, verify_meta=False | ||
).persist() | ||
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mod_score = dask.array.from_delayed( | ||
part_mod_score[0][1], shape=(1,), dtype=float | ||
).compute() | ||
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wait(ddf) | ||
wait(mod_score) | ||
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wait([r.release() for r in part_mod_score]) | ||
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if input_graph.renumbered: | ||
ddf = input_graph.unrenumber(ddf, "vertex") | ||
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return ddf, mod_score |
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