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clustering_preprocessing.py
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clustering_preprocessing.py
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import utils
import directories
import shutil
import timer
import numpy as np
from collections import defaultdict
BATCH_SIZE = 8192
SCORE_THRESHOLD = -0.5 if directories.CHINESE else -2
MARGIN_THRESHOLD = -1 if directories.CHINESE else -1.5
class ActionSpace:
def __init__(self, did, actions, possible_pairs):
self.did = did
self.actions = actions
self.possible_pairs = possible_pairs
self.mentions = [action[0] for action in actions]
def load(self, data, pair_model, anaphoricity_model):
timer.start("pair model")
pair_features, self.pair_ids = data.vectorize_pairs(self.did, self.possible_pairs)
self.pair_vectors = run_static_model(pair_features, pair_model)
timer.stop("pair model")
timer.start("anaphoricity model")
mention_features, self.mention_ids = data.vectorize_mentions(self.did, self.mentions)
self.mention_vectors = run_static_model(mention_features, anaphoricity_model)
timer.stop("anaphoricity model")
def clear(self):
self.pair_vectors = self.pair_ids = None
self.mention_vectors = self.mention_ids = None
def get_pair_features(self, m1, m2):
i = self.pair_ids[(m1, m2)] if (m1, m2) in self.pair_ids else \
self.pair_ids[(m2, m1)]
return self.pair_vectors[i]
def get_mention_features(self, m):
return self.mention_vectors[self.mention_ids[m]][np.newaxis]
def run_static_model(features, model):
if model is None:
return features
vectors = []
for i in range(1 + features.shape[0] / BATCH_SIZE):
timer.start("pair model")
vectors.append(model.predict_on_batch(
{'X': features[i * BATCH_SIZE:(i + 1) * BATCH_SIZE]})[0])
vectors = np.vstack(vectors)
assert vectors.shape[0] == features.shape[0]
return vectors
def get_possible_pairs(probable_pairs):
m_to_maxcluster = {}
for m1, m2 in probable_pairs:
c1 = m_to_maxcluster[m1] if m1 in m_to_maxcluster else {m1}
c2 = m_to_maxcluster[m2] if m2 in m_to_maxcluster else {m2}
if c1 != c2:
c = c1 | c2
for m in c:
m_to_maxcluster[m] = c
maxclusters = set()
for mc in m_to_maxcluster.values():
mc = tuple(mc)
if mc not in maxclusters:
maxclusters.add(mc)
mentions = set()
for m1, m2 in probable_pairs:
mentions.add(m1)
mentions.add(m2)
assert len(mentions) == sum(len(mc) for mc in maxclusters)
possible_pairs = []
for mc in maxclusters:
for m1 in mc:
for m2 in mc:
if m1 < m2:
possible_pairs.append((m1, m2))
return possible_pairs
def write_probable_pairs(dataset_name, action_space_path, scores):
probable_pairs = {}
margin_removals = 0
total_pairs = 0
total_size = 0
for did in utils.logged_loop(scores):
doc_scores = scores[did]
pairs = sorted([pair for pair in doc_scores.keys() if pair[0] != -1],
key=lambda pr: doc_scores[pr] - (-1 - 0.3*doc_scores[(-1, pr[1])]),
reverse=True)
total_pairs += len(pairs)
probable_pairs[did] = []
for pair in pairs:
score = doc_scores[pair] - (-1 - 0.3*doc_scores[(-1, pair[1])])
if score < SCORE_THRESHOLD:
break
probable_pairs[did].append(pair)
max_scores = {}
for pair in probable_pairs[did]:
if pair[1] not in max_scores:
max_scores[pair[1]] = max(doc_scores[pair], -1 - 0.3*doc_scores[(-1, pair[1])])
else:
max_scores[pair[1]] = max(max_scores[pair[1]], doc_scores[pair])
margin_removals += len(probable_pairs[did])
probable_pairs[did] = [p for p in probable_pairs[did] if
doc_scores[p] - max_scores[p[1]] > MARGIN_THRESHOLD]
margin_removals -= len(probable_pairs[did])
total_size += len(probable_pairs[did])
print("num docs:", len(scores))
print("avg size without filter: {:.1f}".format(total_pairs / float(len(scores))))
print("avg size: {:.1f}".format(total_size / float(len(scores))))
print("margin removals size: {:.1f}".format(margin_removals / float(len(scores))))
utils.write_pickle(probable_pairs, action_space_path + dataset_name + '_probable_pairs.pkl')
shutil.copyfile('clustering_preprocessing.py',
action_space_path + 'clustering_preprocessing.py')
def write_action_spaces(dataset_name, action_space_path, model_path, ltr=False):
output_file = action_space_path + dataset_name + "_action_space.pkl"
print("Writing candidate actions to " + output_file)
scores = utils.load_pickle(model_path + dataset_name + "_scores.pkl")
write_probable_pairs(dataset_name, action_space_path, scores)
probable_pairs = utils.load_pickle(action_space_path + dataset_name + '_probable_pairs.pkl')
possible_pairs_total = 0
action_spaces = []
for did in scores:
if did in probable_pairs:
actions = defaultdict(list)
for (m1, m2) in probable_pairs[did]:
actions[m2].append(m1)
if ltr:
x = (ana1, ants1)
y = (ana2, ants2)
actions = sorted(actions.items(), key=functools.cmp_to_key(lambda x, y:
-1 if (ana1, ana2) in scores[did] else 1))
for i in range(len(actions) - 1):
assert (actions[i][0], actions[i + 1][0]) in scores[did]
else:
actions = sorted(actions.items(), key=lambda ana, ants:
max(scores[did][(ant, ana)] - scores[did][(-1, ana)]
for ant in ants))
possible_pairs = get_possible_pairs(probable_pairs[did])
possible_pairs_total += len(possible_pairs)
action_spaces.append(ActionSpace(did, actions, possible_pairs))
utils.write_pickle(action_spaces, output_file)
def main(ranking_model):
write_action_spaces("train", directories.ACTION_SPACE,
directories.MODELS + ranking_model + "/")
write_action_spaces("dev", directories.ACTION_SPACE,
directories.MODELS + ranking_model + "/")
write_action_spaces("test", directories.ACTION_SPACE,
directories.MODELS + ranking_model + "/")