Commit c4667d6f authored by Fize Jacques's avatar Fize Jacques

add Parralelization graph embedding

parent a0519606
...@@ -116,7 +116,7 @@ cdef class DeepWalk(Base): ...@@ -116,7 +116,7 @@ cdef class DeepWalk(Base):
def extract_embedding(self, listgs): def extract_embedding(self, listgs):
from tqdm import tqdm from tqdm import tqdm
models = [process(nx.Graph(g)) for g in tqdm(listgs,desc="Extracting Embeddings...")] models = Parallel(n_jobs = cpu_count())(delayed(process)(nx.Graph(g)) for g in tqdm(listgs,desc="Extracting Embeddings..."))
return models return models
@cython.boundscheck(False) @cython.boundscheck(False)
......
...@@ -89,7 +89,7 @@ def generate_model(graphs, iteration = 2, dimensions = 64, min_count = 5, down_s ...@@ -89,7 +89,7 @@ def generate_model(graphs, iteration = 2, dimensions = 64, min_count = 5, down_s
Main function to read the graph list, extract features, learn the embedding and save it. Main function to read the graph list, extract features, learn the embedding and save it.
:param args: Object with the arguments. :param args: Object with the arguments.
""" """
document_collections = [feature_extractor(g, ix,iteration) for ix,g in tqdm(enumerate(graphs),desc="Extracting Features...")] document_collections = Parallel(n_jobs = workers)(delayed(feature_extractor)(g, ix,iteration) for ix,g in tqdm(enumerate(graphs),desc="Extracting Features..."))
graphs=[nx.relabel_nodes(g,{node:str(node) for node in list(g.nodes)},copy=True) for g in graphs] graphs=[nx.relabel_nodes(g,{node:str(node) for node in list(g.nodes)},copy=True) for g in graphs]
model = Doc2Vec(document_collections, model = Doc2Vec(document_collections,
vector_size = dimensions, vector_size = dimensions,
......
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