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node2vec.py
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node2vec.py
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from itertools import chain, repeat
from inspect import cleandoc
from typing import List, Dict
import gensim
import mgp
from mage.node2vec.second_order_random_walk import SecondOrderRandomWalk
from mage.node2vec.graph import GraphHolder, Graph
class Parameters:
VECTOR_SIZE = "vector_size"
WINDOW = "window"
MIN_COUNT = "min_count"
WORKERS = "workers"
MIN_ALPHA = "min_alpha"
SEED = "seed"
ALPHA = "alpha"
EPOCHS = "epochs"
SG = "sg"
HS = "hs"
NEGATIVE = "negative"
NODE_EMBEDDING_PROPERTY = "embedding"
def learn_embeddings(
walks: List[List[int]], **word2vec_params
) -> Dict[int, List[float]]:
model = gensim.models.Word2Vec(sentences=walks, **word2vec_params)
embeddings = {
index: embedding
for index, embedding in zip(model.wv.index_to_key, model.wv.vectors)
}
return embeddings
def calculate_node_embeddings(
graph: Graph,
p: float,
q: float,
num_walks: int,
walk_length: int,
vector_size: int,
alpha: float,
window: int,
min_count: int,
seed: int,
workers: int,
min_alpha: float,
sg: int,
hs: int,
negative: int,
epochs: int,
) -> Dict[int, List[float]]:
word2vec_params = {
Parameters.VECTOR_SIZE: vector_size,
Parameters.WINDOW: window,
Parameters.MIN_COUNT: min_count,
Parameters.WORKERS: workers,
Parameters.MIN_ALPHA: min_alpha,
Parameters.SEED: seed,
Parameters.ALPHA: alpha,
Parameters.EPOCHS: epochs,
Parameters.SG: sg,
Parameters.HS: hs,
Parameters.NEGATIVE: negative,
}
second_order_random_walk = SecondOrderRandomWalk(
p=p, q=q, num_walks=int(num_walks), walk_length=int(walk_length)
)
walks = second_order_random_walk.sample_node_walks(graph)
embeddings = learn_embeddings(walks, **word2vec_params)
return embeddings
def get_graph_memgraph_ctx(
ctx: mgp.ProcCtx, edge_weight_property: str, is_directed: bool = False
) -> Graph:
edges_weights = {}
for vertex in ctx.graph.vertices:
for edge in vertex.out_edges:
edge_weight = float(edge.properties.get(edge_weight_property, default=1))
old_value = 0
if (edge.from_vertex.id, edge.to_vertex.id) in edges_weights:
old_value = edges_weights[(edge.from_vertex.id, edge.to_vertex.id)]
edges_weights[(edge.from_vertex.id, edge.to_vertex.id)] = (
old_value + edge_weight
)
graph: Graph = GraphHolder(edges_weights, is_directed)
return graph
@mgp.read_proc
def get_embeddings(
ctx: mgp.ProcCtx,
is_directed: bool = False,
p=2.0,
q=0.5,
num_walks=4,
walk_length=5,
vector_size=100,
alpha=0.025,
window=5,
min_count=1,
seed=1,
workers=1,
min_alpha=0.0001,
sg=1,
hs=0,
negative=5,
epochs=5,
edge_weight_property="weight",
) -> mgp.Record(nodes=mgp.List[mgp.Vertex], embeddings=mgp.List[mgp.List[mgp.Number]]):
"""
Function to get node embeddings. Uses gensim.models.Word2Vec params.
Parameters
----------
is_directed : bool, optional
If bool=True, graph is treated as directed, else not directed
p : float, optional
Return hyperparameter for calculating transition probabilities.
q : float, optional
Inout hyperparameter for calculating transition probabilities.
num_walks : int, optional
Number of walks per node in walk sampling.
walk_length : int, optional
Length of one walk in walk sampling.
vector_size : int, optional
Dimensionality of the word vectors.
window : int, optional
Maximum distance between the current and predicted word within a sentence.
min_count : int, optional
Ignores all words with total frequency lower than this.
workers : int, optional
Use these many worker threads to train the model (=faster training with multicore machines).
sg : {0, 1}, optional
Training algorithm: 1 for skip-gram; otherwise CBOW.
hs : {0, 1}, optional
If 1, hierarchical softmax will be used for model training.
If 0, and `negative` is non-zero, negative sampling will be used.
negative : int, optional
If > 0, negative sampling will be used, the int for negative specifies how many "noise words"
should be drawn (usually between 5-20).
If set to 0, no negative sampling is used.
cbow_mean : {0, 1}, optional
If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used.
alpha : float, optional
The initial learning rate.
min_alpha : float, optional
Learning rate will linearly drop to `min_alpha` as training progresses.
seed : int, optional
Seed for the random number generator. Initial vectors for each word are seeded with a hash of
the concatenation of word + `str(seed)`.
edge_weight_property: str,
Property from graph in database from which you want to take edge weights.
"""
graph: Graph = get_graph_memgraph_ctx(
ctx=ctx, is_directed=is_directed, edge_weight_property=edge_weight_property
)
embeddings = calculate_node_embeddings(
graph=graph,
p=p,
q=q,
num_walks=num_walks,
walk_length=walk_length,
vector_size=vector_size,
alpha=alpha,
window=window,
min_count=min_count,
seed=seed,
workers=workers,
min_alpha=min_alpha,
sg=sg,
hs=hs,
negative=negative,
epochs=epochs,
)
embeddings_result = []
nodes_result = []
for node_id, embedding in embeddings.items():
embeddings[node_id] = [float(e) for e in embedding]
nodes_result.append(ctx.graph.get_vertex_by_id(node_id))
embeddings_result.append(embeddings[node_id])
# TODO (antoniofilipovic): when api becomes available, change to return list of records
return mgp.Record(nodes=nodes_result, embeddings=embeddings_result)
@mgp.write_proc
def set_embeddings(
ctx: mgp.ProcCtx,
is_directed: bool = False,
p=2.0,
q=0.5,
num_walks=4,
walk_length=5,
vector_size=100,
alpha=0.025,
window=5,
min_count=1,
seed=1,
workers=1,
min_alpha=0.0001,
sg=1,
hs=0,
negative=5,
epochs=5,
edge_weight_property="weight",
) -> mgp.Record(nodes=mgp.List[mgp.Vertex], embeddings=mgp.List[mgp.List[mgp.Number]]):
"""
Function to get node embeddings. Uses gensim.models.Word2Vec params.
Parameters
----------
edges : List[mgp.Edge]
All the edges in graph.
is_directed : bool, optional
If bool=True, graph is treated as directed, else not directed
p : float, optional
Return hyperparameter for calculating transition probabilities.
q : float, optional
Inout hyperparameter for calculating transition probabilities.
num_walks : int, optional
Number of walks per node in walk sampling.
walk_length : int, optional
Length of one walk in walk sampling.
vector_size : int, optional
Dimensionality of the word vectors.
window : int, optional
Maximum distance between the current and predicted word within a sentence.
min_count : int, optional
Ignores all words with total frequency lower than this.
workers : int, optional
Use these many worker threads to train the model (=faster training with multicore machines).
sg : {0, 1}, optional
Training algorithm: 1 for skip-gram; otherwise CBOW.
hs : {0, 1}, optional
If 1, hierarchical softmax will be used for model training.
If 0, and `negative` is non-zero, negative sampling will be used.
negative : int, optional
If > 0, negative sampling will be used, the int for negative specifies how many "noise words"
should be drawn (usually between 5-20).
If set to 0, no negative sampling is used.
cbow_mean : {0, 1}, optional
If 0, use the sum of the context word vectors. If 1, use the mean, only applies when cbow is used.
alpha : float, optional
The initial learning rate.
min_alpha : float, optional
Learning rate will linearly drop to `min_alpha` as training progresses.
seed : int, optional
Seed for the random number generator. Initial vectors for each word are seeded with a hash of
the concatenation of word + `str(seed)`.
edge_weight_property: str,
Property from graph in database from which you want to take edge weights.
"""
graph: Graph = get_graph_memgraph_ctx(
ctx=ctx, is_directed=is_directed, edge_weight_property=edge_weight_property
)
embeddings = calculate_node_embeddings(
graph=graph,
p=p,
q=q,
num_walks=num_walks,
walk_length=walk_length,
vector_size=vector_size,
alpha=alpha,
window=window,
min_count=min_count,
seed=seed,
workers=workers,
min_alpha=min_alpha,
sg=sg,
hs=hs,
negative=negative,
epochs=epochs,
)
embeddings_result = []
nodes_result = []
for node_id, embedding in embeddings.items():
embeddings[node_id] = [float(e) for e in embedding]
vertex = ctx.graph.get_vertex_by_id(node_id)
vertex.properties.set(NODE_EMBEDDING_PROPERTY, embeddings[node_id])
nodes_result.append(ctx.graph.get_vertex_by_id(node_id))
embeddings_result.append(embeddings[node_id])
# TODO (antoniofilipovic): when api becomes available, change to return list of records
return mgp.Record(nodes=nodes_result, embeddings=embeddings_result)
@mgp.read_proc
def help() -> mgp.Record(name=str, value=str):
"""Shows manual page for node2vec"""
records = []
def make_records(name, doc):
return (
mgp.Record(name=n, value=v)
for n, v in zip(chain([name], repeat("")), cleandoc(doc).splitlines())
)
for func in (help, get_embeddings):
records.extend(
make_records("Procedure '{}'".format(func.__name__), func.__doc__)
)
return records