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knn_dynamic_benchmark_splits.py
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knn_dynamic_benchmark_splits.py
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import pickle5 as pickle
import time
import pandas as pd
import multiprocess
from tqdm import tqdm
from copy import deepcopy
from ge import DeepWalk
from ge import Node2Vec
from natuke_utils import metapath2vec
from natuke_utils import regularization
from natuke_utils import embedding_graph
from natuke_utils import get_knn_data
from natuke_utils import run_knn
path = 'your-data-path'
file_name = 'knn_results'
def restore_hin_split(G, cutted_df, edge_group,
n_jobs=-1, k=-1, node_feature='node', neighbor_feature='neighbor',
group_feature='group', embedding_feature='f'):
# function
def process(start, end, G, df, edge_group, return_dict, thread_id):
value_thread = df.loc[start:(end-1)]
restored_dict_thread = {'true': [], 'restored': [], 'edge_type': []}
for _, row in tqdm(value_thread.iterrows(), total=value_thread.shape[0]):
edge_to_add = edge_group.split('_')
edge_to_add[0] = row[node_feature]
edge_to_add = [row[node_feature] if e == G.nodes[row[node_feature]][group_feature] and row[node_feature] != edge_to_add[0] else e for e in edge_to_add]
knn_data, knn_nodes = get_knn_data(G, row[node_feature], embedding_feature=embedding_feature)
knn_nodes['type'] = knn_nodes[0].apply(lambda x: G.nodes[x][group_feature])
knn_data = knn_data[knn_nodes['type'].isin(edge_to_add)]
knn_nodes = knn_nodes[knn_nodes['type'].isin(edge_to_add)]
edge_to_add[1] = run_knn(k, G, row, knn_data, knn_nodes, embedding_feature=embedding_feature)
restored_dict_thread['true'].append([row[node_feature], row[neighbor_feature]])
restored_dict_thread['restored'].append(edge_to_add)
restored_dict_thread['edge_type'].append(edge_group)
for key in restored_dict_thread.keys():
_key = key + str(thread_id)
return_dict[_key] = (restored_dict_thread[key])
# split threads
def split_processing(n_jobs, G, df, edge_group, return_dict):
split_size = round(len(df) / n_jobs)
threads = []
for i in range(n_jobs):
# determine the indices of the list this thread will handle
start = i * split_size
# special case on the last chunk to account for uneven splits
end = len(df) if i+1 == n_jobs else (i+1) * split_size
# create the thread
threads.append(
multiprocess.Process(target=process, args=(start, end, G, df, edge_group, return_dict, i)))
threads[-1].start() # start the thread we just created
# wait for all threads to finish
for t in threads:
t.join()
if n_jobs == -1:
n_jobs = multiprocess.cpu_count()
restored_dict = {'true': [], 'restored': [], 'edge_type': []}
return_dict = multiprocess.Manager().dict()
split_processing(n_jobs, G, cutted_df, edge_group, return_dict)
return_dict = dict(return_dict)
for thread_key in restored_dict.keys():
for job in range(n_jobs):
for res in return_dict[thread_key + str(job)]:
restored_dict[thread_key].append(res)
return pd.DataFrame(restored_dict)
def load_graph_train_test(iteration, edge_group, evaluation_stage):
with open(f"{path}splits/kg_{edge_group}_{iteration}_{evaluation_stage}.gpickle", "rb") as fh:
G_found = pickle.load(fh)
train = pd.read_csv(f'{path}splits/train_{edge_group}_{iteration}_{evaluation_stage}.csv')
test = pd.read_csv(f'{path}splits/test_{edge_group}_{iteration}_{evaluation_stage}.csv')
return G_found, train, test
# if a new graph is generated this function can split it according to the splits file
def new_graph_splitter(G, test, extra_cut_from='nubbe', node_from_feature='node_from', type_feature='edge_group'):
G_disturbed = deepcopy(G)
for _, row in test.iterrows():
neighbors_list = list(G_disturbed.neighbors(row['node']))
neighbors_hidden = []
for neighbor in neighbors_list:
if G_disturbed.nodes[neighbor][node_from_feature] == extra_cut_from:
neighbors_hidden.append({'neighbor': neighbor, 'edge_group': G_disturbed[row['node']][neighbor][type_feature]})
G_disturbed.remove_edge(row['node'],neighbor)
return G_disturbed
def execution(algorithm, split, iteration, edge_group, evaluation_stages):
# if a new graph is generated load it here
# with open(f"{path}/your_graph_name.gpickle", "rb") as fh:
# G = pickle.load(fh)
if algorithm == 'deep_walk':
for evaluation_stage in evaluation_stages:
print(f'Evaluation for {algorithm},{split},{iteration},{edge_group},{evaluation_stage}')
G_found, train, test = load_graph_train_test(iteration, edge_group, evaluation_stage)
# if new graph
# G_found = new_graph_splitter(G, test)
start_time = time.time()
model_deep_walk = DeepWalk(G_found, walk_length=10, num_walks=80, workers=1)
model_deep_walk.train(window_size=5, iter=3, embed_size=512)
embeddings_deep_walk = model_deep_walk.get_embeddings()
G_found = embedding_graph(G_found, embeddings_deep_walk)
restored_df = restore_hin_split(G_found, test, edge_group)
with open("{}results/execution_time.txt".format(path), 'a') as f:
f.write(f'{algorithm},{split},{iteration},{edge_group},{evaluation_stage},{(time.time() - start_time)}\n')
restored_df.to_csv("{}results/{}_{}_{}_{}_{}_{}.csv".format(path, file_name, algorithm, split, edge_group, iteration, evaluation_stage), index=False)
elif algorithm == 'node2vec':
for evaluation_stage in evaluation_stages:
print(f'Evaluation for {algorithm},{split},{iteration},{edge_group},{evaluation_stage}')
G_found, train, test = load_graph_train_test(iteration, edge_group, evaluation_stage)
# if new graph
# G_found = new_graph_splitter(G, test)
start_time = time.time()
model_node2vec = Node2Vec(G_found, walk_length = 10, num_walks = 80, p = 0.5, q = 1, workers = 1)
model_node2vec.train(window_size=5,iter=3,embed_size=512)
embeddings_node2vec = model_node2vec.get_embeddings()
G_found = embedding_graph(G_found, embeddings_node2vec)
restored_df = restore_hin_split(G_found, test, edge_group)
with open("{}results/execution_time.txt".format(path), 'a') as f:
f.write(f'{algorithm},{split},{iteration},{edge_group},{evaluation_stage},{(time.time() - start_time)}\n')
restored_df.to_csv("{}results/{}_{}_{}_{}_{}_{}.csv".format(path, file_name, algorithm, split, edge_group, iteration, evaluation_stage), index=False)
elif algorithm == 'metapath2vec':
for evaluation_stage in evaluation_stages:
print(f'Evaluation for {algorithm},{split},{iteration},{edge_group},{evaluation_stage}')
G_found, train, test = load_graph_train_test(iteration, edge_group, evaluation_stage)
# if new graph
# G_found = new_graph_splitter(G, test)
start_time = time.time()
embeddings_metapath2vec = metapath2vec(G_found, dimensions=512)
G_found = embedding_graph(G_found, embeddings_metapath2vec)
restored_df = restore_hin_split(G_found, test, edge_group)
with open("{}results/execution_time.txt".format(path), 'a') as f:
f.write(f'{algorithm},{split},{iteration},{edge_group},{evaluation_stage},{(time.time() - start_time)}\n')
restored_df.to_csv("{}results/{}_{}_{}_{}_{}_{}.csv".format(path, file_name, algorithm, split, edge_group, iteration, evaluation_stage), index=False)
elif algorithm == 'regularization':
iterations = 30
for evaluation_stage in evaluation_stages:
print(f'Evaluation for {algorithm},{split},{iteration},{edge_group},{evaluation_stage}')
G_found, train, test = load_graph_train_test(iteration, edge_group, evaluation_stage)
# if new graph
# G_found = new_graph_splitter(G, test)
start_time = time.time()
G_found = regularization(G_found, iterations=iterations, mi=0.85)
restored_df = restore_hin_split(G_found, test, edge_group)
with open("{}results/execution_time.txt".format(path), 'a') as f:
f.write(f'{algorithm},{split},{iteration},{edge_group},{evaluation_stage},{(time.time() - start_time)}\n')
restored_df.to_csv("{}results/{}_{}_{}_{}_{}_{}.csv".format(path, file_name, algorithm, split, edge_group, iteration, evaluation_stage), index=False)
iterations = 20
if __name__ == '__main__':
# just to be compatible with execution time codes
split = 0.8
#edge_groups = ['doi_name', 'doi_bioActivity', 'doi_collectionSpecie', 'doi_collectionSite', 'doi_collectionType']
edge_groups = ['doi_bioActivity']
#algorithms = ['deep_walk', 'node2vec', 'metapath2vec', 'regularization']
algorithms = ['deep_walk', 'node2vec', 'metapath2vec', 'regularization']
evaluation_stages = ['1st', '2nd', '3rd', '4th']
# regularization
for iteration in range(10):
for edge_group in edge_groups:
for algorithm in algorithms:
execution(algorithm, split, iteration, edge_group, evaluation_stages)