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HashAlign.py
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HashAlign.py
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# [ Imports ]
# [ -Python ]
from pathlib2 import Path
import os.path
import pickle
import time
import sys
import warnings
# [ -Third Party ]
import pandas as pd
import numpy as np
# [ -Project ]
from utils.lsh_utils import \
computeWholeSimMat, generateCosineBuckets, generateEuclideanBuckets,\
selectAndCombineMulti, combineBucketsBySumMulti, sparseRank, computeSparseMatchingMat
class HashAlign:
def __init__(self, fname):
self.sim_matrix = {}
self.best_ranking = {}
self.best_correct_match = {}
self.fname = 'exp_result/' + fname
def experiment(self, df, filename, bandNumber=4, LSHType='Euclidean',
loop_num=1, cos_num_plane=50, euc_width=3, compute_sim=False,
threshold=0.2, reweight=True):
## debug
np.seterr(all='raise')
warnings.filterwarnings('error')
# Load all necessary data
metadata = {}
centers = []
# Load synthetic graph information
with open('./private_data/' + filename + '/metadata') as f:
for line in f:
line = line.strip().split()
metadata[line[0]] = line[1]
# Check multiple graphs
if metadata['number'] >= 1:
with open('./private_data/' + filename + '/centers') as f:
for line in f:
centers.append(line.strip().split()[0])
f.close()
else:
raise RuntimeError("Need two graphs to align")
# Load all graph attributes
graph_attrs = pickle.load(open('./private_data/' + filename + '/attributes.pkl', 'rb'))
graph_perm = pickle.load(open('./private_data/' + filename + '/permutations.pkl', 'rb'))
multi_graphs = pickle.load(open('./private_data/' + filename + '/multi_graphs.pkl', 'rb'))
if Path('./private_data/' + filename + '/node_label.pkl').exists():
node_label = pickle.load(open('./private_data/' + filename + '/node_label.pkl', 'rb'))
# Load attributes name
attributes = []
with open('./private_data/' + filename + '/attributes') as f:
for line in f:
attributes.append(line.strip().split()[0])
for center_id in centers:
rank_score = 0
rank_score_upper = 0
correct_score = 0
correct_score_upper = 0
pairs_computed = 0
avg_derived_rank = 0
start_sim = time.time()
if compute_sim:
for g in graph_attrs.keys():
if (center_id, g) not in self.sim_matrix and g != center_id:
print('!!! computed self.sim_matrix !!!')
self.sim_matrix[(center_id, g)] = computeWholeSimMat(graph_attrs[center_id], graph_attrs[g],
LSHType)
if (center_id, g) not in self.best_ranking and g != center_id:
self.best_ranking[(center_id, g)], self.best_correct_match[(center_id, g)] = sparseRank(
self.sim_matrix[(center_id, g)], graph_perm[center_id], graph_perm[g])
end_sim = time.time()
print('sim_time: ' + str(end_sim - start_sim))
start_matching = time.time()
for i in range(loop_num):
## ------------ generate buckets ------------ ##
band_all = list(attributes)
np.random.shuffle(band_all)
randomBand = [band_all[i * len(band_all) / bandNumber: (i + 1) * len(band_all) / bandNumber] for i in
range(bandNumber)]
buckets = []
if LSHType == 'Cosine':
for band in randomBand:
buckets.append(generateCosineBuckets(selectAndCombineMulti(graph_attrs, band), cos_num_plane))
elif LSHType == 'Euclidean':
for band in randomBand:
buckets.append(generateEuclideanBuckets(selectAndCombineMulti(graph_attrs, band), euc_width))
## ---------- end generate buckets ----------- ##
stacked_attrs = selectAndCombineMulti(graph_attrs)
pair_count_dict = combineBucketsBySumMulti(buckets, stacked_attrs[['Graph', 'Id']], graph_attrs.keys(),
center_id, reweight)
matching_matrix = {}
this_pair_computed = {}
Ranking = {}
correctMatch = {}
for g in pair_count_dict.keys():
if g == center_id:
continue
matching_matrix[g], this_pair_computed[g] \
= computeSparseMatchingMat(graph_attrs[center_id], graph_attrs[g], pair_count_dict[g], LSHType,
threshold)
Ranking[g], correctMatch[g] = sparseRank(matching_matrix[g], graph_perm[center_id], graph_perm[g])
rank_score += sum(Ranking[g]) / len(Ranking[g])
correct_score += sum(correctMatch[g]) / float(len(correctMatch[g]))
if not compute_sim:
self.best_ranking[(center_id, g)] = Ranking[g]
self.best_correct_match[(center_id, g)] = correctMatch[g]
rank_score_upper += 0
correct_score_upper += 0
else:
rank_score_upper += sum(self.best_ranking[(center_id, g)]) / len(
self.best_ranking[(center_id, g)])
correct_score_upper += sum(self.best_correct_match[(center_id, g)]) / float(
len(self.best_correct_match[(center_id, g)]))
pairs_computed += this_pair_computed[g] / float(
matching_matrix[g].shape[0] * matching_matrix[g].shape[1])
print("==========================================================")
print(filename + ' ' + g + ', center:' + center_id + ', center_dist: ' + metadata[
'center_distance'])
print("GraphType = " + metadata['graph_type'])
print("bandNumber = " + str(bandNumber) + ", LSHType = " + LSHType)
print("noise_level = " + metadata['noise_level'] + ", nodeAttributeFile = " + metadata[
'node_dir'] + ", threshold = " + str(threshold))
print("matching score by ranking: %f" % (sum(Ranking[g]) / len(Ranking[g])))
if compute_sim:
print("matching score by ranking upper bound: %f" % (
sum(self.best_ranking[(center_id, g)]) / len(self.best_ranking[(center_id, g)])))
print("matching score by correct match: %f" % (sum(correctMatch[g]) / float(len(correctMatch[g]))))
if compute_sim:
print(
"matching score by correct match upper bound %f" % (
sum(self.best_correct_match[(center_id, g)])
/ float(len(self.best_correct_match[(center_id, g)]))
)
)
print("percentage of pairs computed: %f" % (this_pair_computed[g] / float(
matching_matrix[g].shape[0] * matching_matrix[g].shape[1])))
if int(metadata['number']) > 1:
derived_matching_matrix = {}
derived_rank = {}
non_center = matching_matrix.keys()
for i in range(len(non_center)):
for j in range(i + 1, len(non_center)):
derived_matching_matrix[(non_center[i], non_center[j])] = matching_matrix[
non_center[i]].T.dot(matching_matrix[non_center[j]])
Ranking, correct_match = sparseRank(derived_matching_matrix[(non_center[i], non_center[j])],
graph_perm[non_center[i]], graph_perm[non_center[j]])
derived_rank[(non_center[i], non_center[j])] = sum(Ranking) / len(Ranking)
print('derived rank score: ')
print(derived_rank)
tmp_avg_derived_rank = sum([v for k, v in derived_rank.iteritems()]) / len(derived_rank)
avg_derived_rank += tmp_avg_derived_rank
print('avg derived rank score: ' + str(tmp_avg_derived_rank))
rank_score /= loop_num * len(pair_count_dict.keys())
rank_score_upper /= loop_num * len(pair_count_dict.keys())
correct_score /= loop_num * len(pair_count_dict.keys())
correct_score_upper /= loop_num * len(pair_count_dict.keys())
pairs_computed /= loop_num * len(pair_count_dict.keys())
avg_derived_rank /= loop_num
end_matching = time.time()
matching_time = end_matching - start_matching
print("matching_time: {}".format(matching_time))
df = df.append({'filename': filename
, 'nodeAttributeFile': metadata['node_dir']
, 'edge_label_dir': metadata['edge_label_dir']
, 'noise_level': metadata['noise_level']
, 'GraphType': metadata['graph_type']
, 'bandNumber': bandNumber
, 'LSHType': LSHType
, 'cos_num_plane': cos_num_plane
, 'euc_width': euc_width
, 'threshold': threshold
, 'rank_score': rank_score
, 'rank_score_upper': rank_score_upper
, 'correct_score': correct_score
, 'correct_score_upper': correct_score_upper
, 'center_id': center_id
, 'found_center': metadata['found_center']
, 'avg_derived_rank': avg_derived_rank
, 'center_dist': metadata['center_distance']
, 'pairs_computed': pairs_computed
, 'preprocess_time': metadata['preprocess_time']
, 'matching_time': matching_time
},
ignore_index=True)
return df
def run(self, band_numbers=[4], cos_num_plane=[25], euc_width=[4], LSHs=['Cosine'],
folders=['facebook'], thresholds=[0.2]):
# center_distance_types = ['canberra', 'manhattan', 'euclidean']
if os.path.isfile(self.fname + '.pkl'):
with open(self.fname + '.pkl', 'rb') as f:
df = pickle.load(f)
else:
df = pd.DataFrame()
for fold in folders:
for band in band_numbers:
for thres in thresholds:
for lsh in LSHs:
if lsh == 'Cosine':
for c in cos_num_plane:
df = self.experiment(df, filename=fold,
bandNumber=band, LSHType=lsh, cos_num_plane=c, threshold=thres,
compute_sim=False)
else:
for e in euc_width:
df = self.experiment(df, filename=fold,
bandNumber=band, LSHType=lsh, euc_width=e, threshold=thres,
compute_sim=False)
Path('exp_result').mkdir(exist_ok=True)
pickle.dump(df, open(self.fname + '.pkl', 'wb'))
df.to_csv(self.fname + '.csv')
self.sim_matrix = {}
self.best_ranking = {}
self.best_correct_match = {}
if __name__ == '__main__':
ha_runner = HashAlign(fname=sys.argv[1])
ha_runner.run()