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eval-embedding.py
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eval-embedding.py
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#!/usr/bin/env python
import argparse
from collections import defaultdict
from itertools import combinations
import time
import datetime
import numpy as np
import networkx as nx
from sklearn.neighbors import BallTree
from lib.graph import read_graph_from_file, read_embeddings_from_file, distance, make_edge
def evaluate_embeddings(embeddings, edges, cda=True, greedy_routing=False, cda_max_vertices=1000, gr_max_pairs=10000, eval_core=False, core_exponent=0.5):
"evaluate quality of embeddings compared with real elge set"
report = []
# get connected component
true_graph = nx.Graph()
true_graph.add_edges_from(edges)
Gcc=sorted(nx.connected_component_subgraphs(true_graph), key=len, reverse=True)
true_graph=Gcc[0]
# use BallTree for efficient graph construction
print "construct BallTree"
vertices = list(true_graph.nodes())
edges = list(true_graph.edges())
n = len(vertices)
if eval_core:
vertices = [v for v in vertices if true_graph.degree(v) >= n**core_exponent]
true_graph = true_graph.subgraph(vertices)
edges = list(true_graph.edges())
embeddings_array = np.array([embeddings[v] for v in vertices])
bt = BallTree(embeddings_array, metric=distance)
degrees = defaultdict(int)
print "compute number of correct directed arcs"
for v1, v2 in edges:
degrees[v1] += 1
degrees[v2] += 1
# compute number of correct DIRECTED arcs assuming that degrees are known
if cda:
all_correct_arcs = set()
cda_vertices = vertices[:]
if len(cda_vertices) > cda_max_vertices:
np.random.shuffle(cda_vertices)
cda_vertices = cda_vertices[:cda_max_vertices]
for v_i, v in enumerate(cda_vertices):
start = time.time()
degree = degrees[v]
dist, ind = bt.query(np.array(embeddings[v]).reshape(1,-1), k=degree+1) # one of neighbors is vertex inself
neigh = [vertices[i] for i in ind[0].tolist() if vertices[i] != v]
for ne in neigh:
if make_edge(v, ne) in edges:
all_correct_arcs.add((v, ne))
finish = time.time()
#print "DEBUG: {} / {}, time={}s".format(v_i + 1, len(cda_vertices), datetime.timedelta(seconds=finish-start))
report.append(['ratio of correct arcs for known degrees', float(len(all_correct_arcs)) / (2 * len(edges))])
if greedy_routing:
print "compute greedy routing efficiency"
random_pairs = set()
if n * (n-1) / 2 <= gr_max_pairs:
random_pairs = set(combinations(vertices, 2))
else:
while(len(random_pairs) < gr_max_pairs):
v1 = np.random.choice(vertices)
v2 = np.random.choice(vertices)
if v1 != v2:
random_pairs.add((v1, v2))
total_distribution = defaultdict(int)
success_distribution = defaultdict(int)
complete_fails_distribution = defaultdict(int)
all_path_length_pairs = defaultdict(int)
for i, pair in enumerate(random_pairs):
src, dst = pair
# best path
best_path_length = nx.shortest_path_length(true_graph, source=src, target=dst)
total_distribution[0] += 1
total_distribution[best_path_length] += 1
# greedy path
curr_src = src
path_length = 0
seen = set()
while curr_src != dst:
seen.add(curr_src)
# find neighbor closest to destination
unseen_neighbors = filter(lambda x: x not in seen, true_graph.neighbors(curr_src))
if not len(unseen_neighbors):
# greedy algorithm stuck in 'leaf'
path_length = np.nan
break
def curr_distance(v):
return distance(embeddings[dst], embeddings[v])
closest_neigh = min(unseen_neighbors, key=curr_distance)
path_length += 1
curr_src = closest_neigh
if path_length == best_path_length:
success_distribution[0] += 1
success_distribution[best_path_length] += 1
if np.isnan(path_length):
complete_fails_distribution[0] += 1
complete_fails_distribution[best_path_length] += 1
all_path_length_pairs[(best_path_length, path_length)] += 1
all_success = success_distribution[0]
all_complete_fails = complete_fails_distribution[0]
all_total = total_distribution[0]
all_ratio = float(all_success) / all_total * 100
print "Complete fails: {} / {} ({:.2f} %)".format(all_complete_fails, all_total, float(all_complete_fails) / all_total * 100)
print "Success: {} / {} ({:.2f} %)".format(all_success, all_total, all_ratio)
for pl in sorted(set(total_distribution.keys()) | set(success_distribution.keys())):
if pl == 0:
continue
total = total_distribution.get(pl, 0)
success = success_distribution.get(pl, 0)
ratio = float(success) / total * 100
print "Success at path length = {}: {} / {} ({:.2f} %)".format(pl, success, total, ratio)
if False:
# depends on R, bad for subgraphs -- not used
n = len(vertices)
R = 2 * np.log(n)
coshR = np.cosh(R)
predicted_edges = set()
print "predict edges"
for v in vertices:
coords = embeddings[v]
neigh_idx = bt.query_radius(coords, R)
neigh = [vertices[i] for i in neigh_idx[0].tolist() if vertices[i] != v]
predicted_edges.update([make_edge(v, ne) for ne in neigh])
report.append(['total_predicted_edges', len(predicted_edges)])
# contingency matrix
print "compute contingency matrix"
report.append(['true positive', len(edges & predicted_edges)])
report.append(['false positive', len(predicted_edges - edges)])
report.append(['false negative', len(edges - predicted_edges)])
report.append(['true negative', n*(n-1)/2 - len(edges | predicted_edges)])
return report
def main():
parser = argparse.ArgumentParser()
parser.add_argument('graph_file')
parser.add_argument('embeddings_file')
parser.add_argument('--deg', action='store_true', help='angle in degrees')
parser.add_argument('--cda', action='store_true', help='correct directed arcs')
parser.add_argument('--cda-max-vertices', type=int, default=1000)
parser.add_argument('--gr', action='store_true', help='evaluate greedy routing')
parser.add_argument('--gr-max-pairs', type=int, default=10000)
parser.add_argument('--core', action='store_true', help='evaluate core')
parser.add_argument('--core-exponent', type=float, help='core exponent', default=0.5)
parser.add_argument('--seed', type=int, help='random seed', default=42)
args = parser.parse_args()
np.random.seed(args.seed)
vertices, edges = read_graph_from_file(args.graph_file)
skip_lines=0
if args.deg:
skip_lines=2
embeddings = read_embeddings_from_file(args.embeddings_file, skip_lines, degrees=args.deg)
print "Evaluate embeddings"
report = evaluate_embeddings(embeddings, edges,
cda=args.cda, cda_max_vertices=args.cda_max_vertices,
greedy_routing=args.gr, gr_max_pairs=args.gr_max_pairs,
eval_core=args.core, core_exponent=args.core_exponent
)
print 'report'
for name, value in report:
print '{}: {}'.format(name, value)
if __name__ == '__main__':
main()