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zhishi_eval.py
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zhishi_eval.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
import os
import sys
import random
from time import strftime, gmtime
import cPickle as pickle
from keras.optimizers import RMSprop, Adam
from scipy.stats import rankdata
from keras.utils import generic_utils
from keras_models import *
from heapq import nlargest
import codecs
random.seed(42)
os.environ['ZHISHI'] = 'data/zhishi'
class Evaluator:
def __init__(self, conf=None):
try:
data_path = os.environ['ZHISHI']
except KeyError:
print("ZHISHI is not set.")
sys.exit(1)
self.path = data_path
self.conf = dict() if conf is None else conf
self.params = conf.get('training_params', dict())
self.entity = self.load('zhishi-id2entity.pkl')
self._vocab = None
self._reverse_vocab = None
self._eval_sets = None
##### Resources #####
def load(self, name):
return pickle.load(open(os.path.join(self.path, name), 'rb'))
def vocab(self):
if self._vocab is None:
self._vocab = self.load('vocabulary')
return self._vocab
def reverse_vocab(self):
if self._reverse_vocab is None:
vocab = self.vocab()
self._reverse_vocab = dict((v.lower(), k) for k, v in vocab.items())
return self._reverse_vocab
##### Loading / saving #####
def save_epoch(self, model, epoch):
if not os.path.exists('models/zhishi_models/embedding/'):
os.makedirs('models/zhishi_models/embedding/')
model.save_weights('models/zhishi_models/embedding/weights_epoch_%d.h5' % epoch, overwrite=True)
def save_epoch_rt(self, model, epoch):
if not os.path.exists('models/zhishi_models/embedding_rt/'):
os.makedirs('models/zhishi_models/embedding_rt/')
model.save_weights_rt('models/zhishi_models/embedding_rt/weights_epoch_%d.h5' % epoch, overwrite=True)
def load_epoch(self, model, epoch):
assert os.path.exists('models/zhishi_models/embedding/weights_epoch_%d.h5' % epoch),\
'Weights at epoch %d not found' % epoch
model.load_weights('models/zhishi_models/embedding/weights_epoch_%d.h5' % epoch)
def load_epoch_rt(self, model, epoch):
assert os.path.exists('models/zhishi_models/embedding_rt/weights_epoch_%d.h5' % epoch),\
'Weights at epoch %d not found' % epoch
model.load_weights_rt('models/zhishi_models/embedding_rt/weights_epoch_%d.h5' % epoch)
##### Converting / reverting #####
def convert(self, words):
rvocab = self.reverse_vocab()
if type(words) == str:
words = words.strip().lower().split(' ')
return [rvocab.get(w, 0) for w in words]
def revert(self, indices):
vocab = self.vocab()
return [vocab.get(i, 'X') for i in indices]
##### Padding #####
def padq(self, data):
return self.pad(data, self.conf.get('question_len', None))
def pada(self, data):
return self.pad(data, self.conf.get('answer_len', None))
def pad(self, data, len=None):
from keras.preprocessing.sequence import pad_sequences
return pad_sequences(data, maxlen=len, padding='post', truncating='post', value=0)
##### Training #####
def print_time(self):
print(strftime('%Y-%m-%d %H:%M:%S :: ', gmtime()), end='')
def train(self, model):
eval_every = self.params.get('eval_every', None)
save_every = self.params.get('save_every', None)
batch_size = self.params.get('batch_size', 128)
nb_epoch = self.params.get('nb_epoch', 10)
print("Load train ...")
training_set = self.load('zhishi-train.pkl')
print("Load entity ...")
entity_candidates = self.entity.keys()
subjects = list()
relations = list()
good_objects = list()
print("Load triplets ...")
for line in training_set:
triplet = line.split('\t')
subjects += [[int(triplet[0])]]
relations += [[int(triplet[1])]]
good_objects += [[int(triplet[2])]]
subjects = np.asarray(subjects)
relations = np.asarray(relations)
good_objects = np.asarray(good_objects)
for i in range(1, nb_epoch+1):
print('-' * 40)
print('Epoch', i)
print('-' * 40)
progbar = generic_utils.Progbar(len(training_set))
nb_batch = int(len(training_set) / batch_size)
for batch_id in xrange(nb_batch):
begin_index = batch_id * batch_size
end_index = len(training_set) if (batch_id + 1) * batch_size > len(training_set) else (batch_id + 1) * batch_size
subjects_batch = subjects[begin_index: end_index]
relations_batch = relations[begin_index: end_index]
good_objects_batch = good_objects[begin_index: end_index]
bad_objects_batch = np.asarray([[int(random.choice(entity_candidates))]
for _ in xrange(end_index - begin_index)])
loss = model.train_on_batch([subjects_batch, relations_batch, good_objects_batch, bad_objects_batch])
progbar.add(subjects_batch.shape[0], values=[('train loss', loss)])
if eval_every is not None and i % eval_every == 0:
self.get_mrr(model)
if save_every is not None and i % save_every == 0:
self.save_epoch(model, i)
print("Training completed!")
def train_rt(self, model):
eval_every = self.params.get('eval_every', None)
save_every = self.params.get('save_every', None)
batch_size = self.params.get('batch_size', 128)
nb_epoch = self.params.get('nb_epoch', 10)
print("Load train ...")
training_set = self.load('zhishi-train.pkl')
print("Load entity ...")
entity_candidates = self.entity.keys()
subjects = list()
relations = list()
good_objects = list()
print("Load triplets ...")
for line in training_set:
triplet = line.split('\t')
subjects += [[int(triplet[0])]]
relations += [[int(triplet[1])]]
good_objects += [[int(triplet[2])]]
subjects = np.asarray(subjects)
relations = np.asarray(relations)
good_objects = np.asarray(good_objects)
for i in range(1, nb_epoch+1):
print('-' * 40)
print('Epoch', i)
print('-' * 40)
progbar = generic_utils.Progbar(len(training_set))
nb_batch = int(len(training_set) / batch_size)
for batch_id in xrange(nb_batch):
begin_index = batch_id * batch_size
end_index = len(training_set) if (batch_id + 1) * batch_size > len(training_set) else (batch_id + 1) * batch_size
subjects_batch = subjects[begin_index: end_index]
bad_subjects_batch = np.asarray([[int(random.choice(entity_candidates))]
for _ in xrange(end_index - begin_index)])
relations_batch = relations[begin_index: end_index]
good_objects_batch = good_objects[begin_index: end_index]
loss = model.train_on_batch_rt([subjects_batch, bad_subjects_batch, relations_batch, good_objects_batch])
progbar.add(subjects_batch.shape[0], values=[('train loss', loss)])
if eval_every is not None and i % eval_every == 0:
self.get_mrr(model)
if save_every is not None and i % save_every == 0:
self.save_epoch_rt(model, i)
print("Training completed!")
##### Evaluation #####
def prog_bar(self, so_far, total, n_bars=20):
n_complete = int(so_far * n_bars / total)
if n_complete >= n_bars - 1:
print('\r[' + '=' * n_bars + ']', end='')
else:
s = '\r[' + '=' * (n_complete - 1) + '>' + '.' * (n_bars - n_complete) + ']'
print(s, end='')
def eval_sets(self):
if self._eval_sets is None:
self._eval_sets = dict([(s, self.load(s)) for s in ['zhishi_hr-test.pkl']])
return self._eval_sets
def eval_sets_rt(self):
if self._eval_sets is None:
self._eval_sets = dict([(s, self.load(s)) for s in ['zhishi_rt-test.pkl']])
return self._eval_sets
def eval_sets_tc(self):
if self._eval_sets is None:
self._eval_sets = dict([(s, self.load(s)) for s in ['zhishi_tc-test.pkl']])
return self._eval_sets
def make_submit(self, model, submit_file):
data = self.eval_sets().values()[0]
target_lines = list()
answers = np.asarray([[idx] for idx in self.entity.keys()])
for i, d in enumerate(data):
num_candidate = len(self.entity)
index_entities = xrange(num_candidate)
terms = d.split('\t')
subjects = np.asarray([[terms[0]]] * num_candidate)
relations = np.asarray([[terms[1]]] * num_candidate)
sims = model.predict([subjects, relations, answers], batch_size=num_candidate).flatten()
print(i)
r = rankdata(sims, method='ordinal')
index_candidates = nlargest(200, index_entities, key=lambda j: r[j])
one_line = ' '.join([str(index_candidate) for index_candidate in index_candidates])
target_lines.append(one_line + '\n')
submit_file.writelines(target_lines)
def make_submit_rt(self, model, submit_file):
data = self.eval_sets_rt().values()[0]
target_lines = list()
answers = np.asarray([[idx] for idx in self.entity.keys()])
for i, d in enumerate(data):
num_candidate = len(self.entity)
index_entities = xrange(num_candidate)
terms = d.split('\t')
relations = np.asarray([[terms[0]]] * num_candidate)
objects = np.asarray([[terms[1]]] * num_candidate)
sims = model.predict_rt([answers, relations, objects], batch_size=num_candidate).flatten()
print(i)
r = rankdata(sims, method='ordinal')
index_candidates = nlargest(200, index_entities, key=lambda j: r[j])
one_line = ' '.join([str(index_candidate) for index_candidate in index_candidates])
target_lines.append(one_line + '\n')
submit_file.writelines(target_lines)
def make_submit_tc(self, model, submit_file):
data = self.eval_sets_tc().values()[0]
target_lines = list()
for i, d in enumerate(data):
terms = d.split('\t')
subjects = np.asarray([[terms[0]]])
relations = np.asarray([[terms[1]]])
objects = np.asarray([[terms[2]]])
sims = model.predict([subjects, relations, objects], batch_size=1).flatten()
print(i)
if sims[0] >= 0.55:
target_lines.append('1')
else:
target_lines.append('0')
submit_file.writelines(' '.join(target_lines))
def get_mrr(self, model, evaluate_all=False):
top1s = list()
mrrs = list()
for name, data in self.eval_sets().items():
if evaluate_all:
self.print_time()
print('----- %s -----' % name)
random.shuffle(data)
if not evaluate_all and 'n_eval' in self.params:
data = data[:self.params['n_eval']]
# c_1 for hit@1, c_3 for hit@3, c_10 for hit@10
c_1, c_3, c_10 = 0, 0, 0
mean_ranks = list()
for i, d in enumerate(data):
triplet = d.split('\t')
if evaluate_all:
self.prog_bar(i, len(data))
candidate_objects = self.entity.keys()
candidate_objects.remove(int(triplet[2]))
subject = np.asarray([[int(triplet[0])]] * (len(candidate_objects)+1))
relation = np.asarray([[int(triplet[1])]] * (len(candidate_objects)+1))
objects = np.asarray([[int(triplet[2])]] + [[entity_id] for entity_id in candidate_objects])
sims = model.predict([subject, relation, objects], batch_size=len(self.entity)).flatten()
r = rankdata(sims, method='max')
target_rank = r[0]
num_candidate = len(sims)
real_rank = num_candidate - target_rank + 1
# print(' '.join(self.revert(d['question'])))
# print(' '.join(self.revert(self.answers[indices[max_r]])))
# print(' '.join(self.revert(self.answers[indices[max_n]])))
c_1 += 1 if target_rank == num_candidate else 0
c_3 += 1 if target_rank + 3 > num_candidate else 0
c_10 += 1 if target_rank + 10 > num_candidate else 0
mean_ranks.append(real_rank)
# c_2 += 1 / float(r[max_r] - r[max_n] + 1)
hit_at_1 = c_1 / float(len(data))
hit_at_3 = c_3 / float(len(data))
hit_at_10 = c_10 / float(len(data))
avg_rank = np.mean(mean_ranks)
del data
if evaluate_all:
print('Hit@1 Precision: %f' % hit_at_1)
print('Hit@3 Precision: %f' % hit_at_3)
print('Hit@10 Precision: %f' % hit_at_10)
print('Mean Rank: %f' % avg_rank)
# top1s.append(top1)
# mrrs.append(mrr)
# rerun the evaluation if above some threshold
if not evaluate_all:
print('Top-1 Precision: {}'.format(top1s))
print('MRR: {}'.format(mrrs))
evaluate_all_threshold = self.params.get('evaluate_all_threshold', dict())
evaluate_mode = evaluate_all_threshold.get('mode', 'all')
mrr_theshold = evaluate_all_threshold.get('mrr', 1)
top1_threshold = evaluate_all_threshold.get('top1', 1)
if evaluate_mode == 'any':
evaluate_all = evaluate_all or any([x >= top1_threshold for x in top1s])
evaluate_all = evaluate_all or any([x >= mrr_theshold for x in mrrs])
else:
evaluate_all = evaluate_all or all([x >= top1_threshold for x in top1s])
evaluate_all = evaluate_all or all([x >= mrr_theshold for x in mrrs])
if evaluate_all:
return self.get_mrr(model, evaluate_all=True)
if __name__ == '__main__':
conf = {
'subject_len': 1,
'relation_len': 1,
'object_len': 1,
'n_words': 648211, # len(vocabulary) + 1
'margin': 0.05,
'training_params': {
'save_every': 100,
# 'eval_every': 1,
'batch_size': 512,
'nb_epoch': 200,
'validation_split': 0,
'optimizer': 'adam',
# 'optimizer': Adam(clip_norm=0.1),
# 'n_eval': 100,
'evaluate_all_threshold': {
'mode': 'all',
'top1': 0.4,
},
},
'model_params': {
'n_embed_dims': 100,
'n_hidden': 200,
# convolution
'nb_filters': 1000, # * 4
'conv_activation': 'relu',
# recurrent
'n_lstm_dims': 141, # * 2
# 'initial_embed_weights': np.load('word2vec_100_dim.embeddings'),
},
'similarity_params': {
'mode': 'cosine',
'gamma': 1,
'c': 1,
'd': 2,
}
}
evaluator = Evaluator(conf)
##### Define model ######
# model = EmbeddingModel(conf)
# optimizer = conf.get('training_params', dict()).get('optimizer', 'adam')
# model.compile(optimizer=optimizer)
model = EmbeddingModelRt(conf)
optimizer = conf.get('training_params', dict()).get('optimizer', 'adam')
model.compile_rt(optimizer=optimizer)
import numpy as np
# save embedding layer
# evaluator.load_epoch(model, 33)
# embedding_layer = model.prediction_model.layers[2].layers[2]
# evaluator.load_epoch(model, 100)
# evaluator.train(model)
# weights = embedding_layer.get_weights()[0]
# np.save(open('models/embedding_1000_dim.h5', 'wb'), weights)
# model for link prediction -> tail
# evaluator.load_epoch(model, 200)
# evaluator.train(model)
# lp_t = codecs.open('lp_t.txt', 'wb')
# evaluator.make_submit(model, lp_t)
# model for link prediction -> head
evaluator.load_epoch_rt(model, 100)
# evaluator.train_rt(model)
lp_h = codecs.open('lp_h.txt', 'wb')
evaluator.make_submit_rt(model, lp_h)
# model for triplet classification
# evaluator.load_epoch(model, 200)
# evaluator.train(model)
# tc = codecs.open('tc.txt', 'wb')
# evaluator.make_submit_tc(model, tc)
# evaluate mrr for a particular epoch
# evaluator.load_epoch(model, 200)
# evaluator.get_mrr(model, evaluate_all=True)