def predict_final(output_key, output_labels):
config_path = data_config.output_path(output_key, ALL, CONFIG)
config_data = yaml.load(open(config_path))
nn_config = NNConfig(config_data)
vocab_id_mapping = json.load(open(data_config.output_path(output_key, ALL, VOCAB_ID_MAPPING), 'r'))
dataset = load_dataset(
mode=FINAL, vocab_id_mapping=vocab_id_mapping,
max_seq_len=nn_config.seq_len, sampling=False, with_label=False
)
index_iterator = SimpleIndexIterator.from_dataset(dataset)
n_sample = index_iterator.n_sample()
with tf.Session() as sess:
prefix_checkpoint = tf.train.latest_checkpoint(data_config.model_path(key=output_key))
saver = tf.train.import_meta_graph('{}.meta'.format(prefix_checkpoint))
saver.restore(sess, prefix_checkpoint)
nn = BaseNNModel(config=None)
nn.set_graph(tf.get_default_graph())
fetches = {_key: nn.var(_key) for _key in [LABEL_PREDICT]}
labels_predict = list()
for batch_index in index_iterator.iterate(nn_config.batch_size, shuffle=False):
feed_dict = {nn.var(_key): dataset[_key][batch_index] for _key in feed_key[TEST]}
feed_dict[nn.var(TEST_MODE)] = 1
res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict)
labels_predict += res[LABEL_PREDICT].tolist()
labels_predict = labels_predict[:n_sample]
with open(output_labels, 'w') as file_obj:
for i, label in enumerate(labels_predict):
file_obj.write('{},{},{}'.format(i, label, label_str[label]))
def live_test(output_key):
config_path = data_config.output_path(output_key, ALL, CONFIG)
config_data = yaml.load(open(config_path))
nn_config = NNConfig(config_data)
vocab_id_mapping = json.load(open(data_config.output_path(output_key, ALL, VOCAB_ID_MAPPING), 'r'))
with tf.Session() as sess:
prefix_checkpoint = tf.train.latest_checkpoint(data_config.model_path(key=output_key))
saver = tf.train.import_meta_graph('{}.meta'.format(prefix_checkpoint))
saver.restore(sess, prefix_checkpoint)
nn = BaseNNModel(config=None)
nn.set_graph(tf.get_default_graph())
fetches = {_key: nn.var(_key) for _key in [LABEL_PREDICT, PROB_PREDICT]}
while True:
res = input('input: ')
if res == 'quit':
break
turns = res.strip().split('|')
if len(turns) != 3:
print('invalid turns')
continue
tokens_list = list()
for turn in turns:
tokens = re.sub('\s+', ' ', turn.strip()).split(' ')
tokens_list.append(tokens)
placeholder = [[]] * (nn_config.batch_size - 1)
tid_list_0 = tokenized_to_tid_list([tokens_list[0], ] + placeholder, vocab_id_mapping)
tid_list_1 = tokenized_to_tid_list([tokens_list[1], ] + placeholder, vocab_id_mapping)
tid_list_2 = tokenized_to_tid_list([tokens_list[2], ] + placeholder, vocab_id_mapping)
tid_0 = np.asarray(zero_pad_seq_list(tid_list_0, nn_config.seq_len))
tid_1 = np.asarray(zero_pad_seq_list(tid_list_1, nn_config.seq_len))
tid_2 = np.asarray(zero_pad_seq_list(tid_list_2, nn_config.seq_len))
feed_dict = {
nn.var(TID_0): tid_0,
nn.var(TID_1): tid_1,
nn.var(TID_2): tid_2,
nn.var(TEST_MODE): 1
}
res = sess.run(fetches=fetches, feed_dict=feed_dict)
label = res[LABEL_PREDICT][0]
prob = res[PROB_PREDICT][0]
print('label: {}'.format(label))
print('prob: {}'.format(prob))
def export_wrong(output_key):
mode = TEST
path = data_config.output_path(output_key, mode, LABEL_PREDICT)
pred = load_label_list(path)
path = data_config.path(mode, LABEL)
gold = load_label_list(path)
tokens_0 = load_tokenized_list(data_config.path(mode, TURN, '0.ek'))
tokens_1 = load_tokenized_list(data_config.path(mode, TURN, '1.ek'))
tokens_2 = load_tokenized_list(data_config.path(mode, TURN, '2.ek'))
wrong = defaultdict(lambda: defaultdict(lambda: list()))
max_seq_len = 0
for p, g, tk_0, tk_1, tk_2 in zip(pred, gold, tokens_0, tokens_1,
tokens_2):
if p != g:
wrong[g][p].append(' '.join(tk_0) + ' | ' + ' '.join(tk_1) +
' | ' + ' '.join(tk_2))
max_seq_len = max(max_seq_len, len(tk_0), len(tk_1), len(tk_2))
for _g in range(4):
for _p in range(4):
print('{}->{}'.format(_g, _p))
for sample in wrong[_g][_p]:
print('\t{}'.format(sample))
print(max_seq_len)
def build_final_submit(output_key):
"""
python -m scripts.semeval2019_task3_dev final -o gru_ek_1543492018
python3 -m scripts.semeval2019_task3_dev build_dev_submit -o
:param output_key: string
:return:
"""
output_path = 'test.txt'
first_line = open(config.path_train, 'r').readline()
with open(output_path, 'w') as o_obj:
o_obj.write(first_line)
lines = open(config.path_test_no_labels).read().strip().split('\n')
lines = lines[1:]
lines = list(map(lambda l: l.strip(), lines))
path_labels = config.output_path(output_key, FINAL, LABEL_PREDICT)
labels = open(path_labels, 'r').read().strip().split('\n')
labels = list(map(int, labels))
labels = list(map(lambda l: label_str[l], labels))
assert len(labels) == len(lines)
for line, label in zip(lines, labels):
o_obj.write('{}\t{}\n'.format(line, label))
def show_config(output_key):
"""
[Usage]
python3 -m algo.main93 config xxxxxx
:param output_key:
:return:
"""
path = data_config.output_path(output_key, ALL, CONFIG)
print(open(path).read())
print(path)
def load_tri_votes(config, modes):
n_sample = 5509
votes = [[0 for _ in range(4)] for _ in range(n_sample)]
for output_key in config.tri:
labels = list()
for _mode in modes:
path = data_config.output_path(output_key, _mode, LABEL_PREDICT)
labels += load_label_list(path)
for i, label in enumerate(labels):
votes[i][label] += 1
return votes
def show_eval(output_key):
labels_predict = list()
labels_gold = list()
for mode in [TRAIN, TEST]:
path = data_config.output_path(output_key, mode, LABEL_PREDICT)
labels_predict += load_label_list(path)
path = data_config.path(mode, LABEL)
labels_gold += load_label_list(path)
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
def show_eval(output_key):
"""
[Usage]
python algo/main.py eval A_ntua_ek_1542454066
:param output_key: string
:return:
"""
for mode in [TRAIN, VALID, TEST]:
res = json.load(
open(data_config.output_path(output_key, mode, EVALUATION)))
print(mode)
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print()
def load_others_votes(config, modes):
n_sample = 5509
votes = [0 for _ in range(n_sample)]
for output_key in config.others:
labels = list()
for _mode in modes:
path = data_config.output_path(output_key, _mode, LABEL_PREDICT)
labels += load_label_list(path)
if len(labels) != n_sample:
raise Exception('mismatch {}({}) != {}'.format(
output_key, len(labels), n_sample))
for i, label in enumerate(labels):
if label == 0:
votes[i] += 1
return votes
def check_wrong(output_key, w2v_key='ntua_ek'):
mode = TEST
path = data_config.output_path(output_key, mode, LABEL_PREDICT)
pred = load_label_list(path)
path = data_config.path(mode, LABEL)
gold = load_label_list(path)
w2v_model_path = data_config.path(ALL, WORD2VEC, w2v_key)
vocab_train_path = data_config.path(TRAIN, VOCAB, 'ek')
# 加载字典集
# 在模型中会采用所有模型中支持的词向量, 并为有足够出现次数的单词随机生成词向量
vocab_meta_list = load_vocab_list(vocab_train_path)
vocabs = [_meta['t'] for _meta in vocab_meta_list if _meta['tf'] >= 2]
# 加载词向量与相关数据
lookup_table, vocab_id_mapping, embedding_dim = load_lookup_table(
w2v_model_path=w2v_model_path, vocabs=vocabs)
tokens_0 = load_tokenized_list(data_config.path(mode, TURN, '0.ek'))
tokens_1 = load_tokenized_list(data_config.path(mode, TURN, '1.ek'))
tokens_2 = load_tokenized_list(data_config.path(mode, TURN, '2.ek'))
tid_list_0 = tokenized_to_tid_list(tokens_0, vocab_id_mapping)
tid_list_1 = tokenized_to_tid_list(tokens_1, vocab_id_mapping)
tid_list_2 = tokenized_to_tid_list(
load_tokenized_list(data_config.path(mode, TURN, '2.ek')),
vocab_id_mapping)
max_seq_len = 0
for p, g, tid_0, tid_1, tid_2, tk_0, tk_1, tk_2 in zip(
pred, gold, tid_list_0, tid_list_1, tid_list_2, tokens_0, tokens_1,
tokens_2):
if p != g and (len(tid_0) > 30 or len(tid_1) > 30 or len(tid_2) > 30):
print('pred: {}, gold: {}'.format(p, g))
print('turn0: {}'.format(' '.join(tk_0)))
print('turn1: {}'.format(' '.join(tk_1)))
print('turn2: {}'.format(' '.join(tk_2)))
if p != g:
max_seq_len = max(max_seq_len, len(tid_0), len(tid_1), len(tid_2))
print(max_seq_len)
def diff(a_filename, b_filename, output_filename, config_path='e93.yaml'):
config_data = yaml.load(open(config_path))
config = Config(data=config_data)
votes = None
for output_key in config.others:
labels = list()
for _mode in modes[FINAL]:
path = data_config.output_path(output_key, _mode, LABEL_PREDICT)
labels += load_label_list(path)
if votes is None:
n_sample = len(labels)
votes = [0 for _ in range(n_sample)]
for i, label in enumerate(labels):
if label == 0:
votes[i] += 1
dataset = Processor.load_origin(a_filename)
labels_a = list(map(lambda _item: _item[-1], dataset))
dataset = Processor.load_origin(b_filename)
labels_b = list(map(lambda _item: _item[-1], dataset))
assert len(votes) == len(labels_a) == len(labels_b)
n_match = 0
with open(output_filename, 'w') as file_obj:
for i, (a, b, d) in enumerate(zip(labels_a, labels_b, dataset)):
if a == 3:
if b == 0:
file_obj.write('{}\t{}\t{}\t{}\t{}->{} ({})\n'.format(
i, d[0], d[1], d[2], label_str[a], label_str[b],
votes[i]))
else:
n_match += 1
print(n_match)
def analyse_others(config_path='e93.yaml'):
config_data = yaml.load(open(config_path))
config = Config(data=config_data)
modes = {
TRAIN: [TRAIN, TEST],
FINAL: [
FINAL,
]
}
for mode in [TRAIN, FINAL]:
n_sample = None
n_effective = 0
for output_key in config.others:
labels = list()
for _mode in modes[mode]:
path = data_config.output_path(output_key, _mode,
LABEL_PREDICT)
labels += load_label_list(path)
if n_sample is None:
n_sample = len(labels)
votes = [0 for _ in range(n_sample)]
for i, label in enumerate(labels):
if label == 0:
votes[i] += 1
if config.others_min_vote == 'all':
min_vote = len(config.others)
else:
min_vote = int(config.others_min_vote)
for i, vote in enumerate(votes):
if vote >= min_vote:
n_effective += 1
print('{}: {}'.format(mode, n_effective))
def filter_by_others(input_filename,
output_filename,
thr,
config_path='e93.yaml'):
thr = int(thr)
config_data = yaml.load(open(config_path))
config = Config(data=config_data)
votes = None
for output_key in config.others:
labels = list()
for _mode in modes[FINAL]:
path = data_config.output_path(output_key, _mode, LABEL_PREDICT)
labels += load_label_list(path)
if votes is None:
n_sample = len(labels)
votes = [0 for _ in range(n_sample)]
for i, label in enumerate(labels):
if label == 0:
votes[i] += 1
dataset = Processor.load_origin(input_filename)
labels = list(map(lambda _item: _item[-1], dataset))
assert len(votes) == len(labels)
with open(output_filename, 'w') as file_obj:
for i, (p, d) in enumerate(zip(labels, dataset)):
if p != 0 and votes[i] >= thr:
file_obj.write('{}\t{}\t{}\t{}\t{} ({})\n'.format(
i, d[0], d[1], d[2], p, votes[i]))
labels[i] = 0
export_final('test.txt', labels)
def show_eval(output_key):
labels_predict_ = dict()
labels_gold_ = dict()
for mode in [TRAIN, TEST, FINAL]:
path = data_config.output_path(output_key, mode, LABEL_PREDICT)
labels_predict_[mode] = load_label_list(path)
path = data_config.path(mode, LABEL)
labels_gold_[mode] = load_label_list(path)
print('TRAIN + TEST')
res = basic_evaluate(gold=labels_gold_[TRAIN] + labels_gold_[TEST],
pred=labels_predict_[TRAIN] + labels_predict_[TEST])
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print()
print('FINAL')
res = basic_evaluate(gold=labels_gold_[FINAL], pred=labels_predict_[FINAL])
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print()
def train(text_version='ek',
label_version=None,
config_path='config93_naive.yaml'):
"""
python -m algo.main93_v2 train
python3 -m algo.main93_v2 train -c config_ntua93.yaml
:param text_version: string
:param label_version: string
:param config_path: string
:return:
"""
config_data = yaml.load(open(config_path))
output_key = '{}_{}_{}'.format(NNModel.name, text_version,
int(time.time()))
if label_version is not None:
output_key = '{}_{}'.format(label_version, output_key)
print('OUTPUT_KEY: {}'.format(output_key))
# 准备输出路径的文件夹
data_config.prepare_output_folder(output_key=output_key)
data_config.prepare_model_folder(output_key=output_key)
shutil.copy(config_path, data_config.output_path(output_key, ALL, CONFIG))
w2v_key = '{}_{}'.format(config_data['word']['w2v_version'], text_version)
w2v_model_path = data_config.path(ALL, WORD2VEC, w2v_key)
vocab_train_path = data_config.path(TRAIN, VOCAB, text_version)
# 加载字典集
# 在模型中会采用所有模型中支持的词向量, 并为有足够出现次数的单词随机生成词向量
vocab_meta_list = load_vocab_list(vocab_train_path)
vocabs = [
_meta['t'] for _meta in vocab_meta_list
if _meta['tf'] >= config_data['word']['min_tf']
]
# 加载词向量与相关数据
lookup_table, vocab_id_mapping, embedding_dim = load_lookup_table2(
w2v_model_path=w2v_model_path, vocabs=vocabs)
json.dump(
vocab_id_mapping,
open(data_config.output_path(output_key, ALL, VOCAB_ID_MAPPING), 'w'))
# 加载配置
nn_config = NNConfig(config_data)
train_config = TrainConfig(config_data['train'])
early_stop_metric = train_config.early_stop_metric
# 加载训练数据
datasets, output_dim = load_dataset(vocab_id_mapping=vocab_id_mapping,
max_seq_len=nn_config.seq_len,
with_label=True,
label_version=label_version)
# 初始化数据集的检索
index_iterators = {
mode: IndexIterator(datasets[mode][LABEL_GOLD])
for mode in [TRAIN, TEST]
}
# 按配置将训练数据切割成训练集和验证集
index_iterators[TRAIN].split_train_valid(train_config.valid_rate)
# 计算各个类的权重
if train_config.use_class_weights:
label_weight = {
# 参考 sklearn 中 class_weight='balanced'的公式, 实验显示效果显着
_label: float(index_iterators[TRAIN].n_sample()) /
(index_iterators[TRAIN].dim * len(_index))
for _label, _index in index_iterators[TRAIN].label_index.items()
}
else:
label_weight = {
_label: 1.
for _label in range(index_iterators[TRAIN].dim)
}
# 基于加载的数据更新配置
nn_config.set_embedding_dim(embedding_dim)
nn_config.set_output_dim(output_dim)
# 搭建神经网络
nn = NNModel(config=nn_config)
nn.build_neural_network(lookup_table=lookup_table)
batch_size = train_config.batch_size
fetches = {
mode: {_key: nn.var(_key)
for _key in fetch_key[mode]}
for mode in [TRAIN, TEST]
}
last_eval = {TRAIN: None, VALID: None, TEST: None}
model_output_prefix = data_config.model_path(key=output_key) + '/model'
best_res = {mode: None for mode in [TRAIN, VALID]}
no_update_count = {mode: 0 for mode in [TRAIN, VALID]}
max_no_update_count = 10
eval_history = {TRAIN: list(), DEV: list(), TEST: list()}
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
dataset = datasets[TRAIN]
index_iterator = index_iterators[TRAIN]
# 训练开始 ##########################################################################
for epoch in range(train_config.epoch):
print('== epoch {} = {} ='.format(epoch, output_key))
# 利用训练集进行训练
print('TRAIN')
n_sample = index_iterator.n_sample(TRAIN)
labels_predict = list()
labels_gold = list()
for batch_index in index_iterator.iterate(batch_size,
mode=TRAIN,
shuffle=True):
feed_dict = {
nn.var(_key): dataset[_key][batch_index]
for _key in feed_key[TRAIN]
}
feed_dict[nn.var(SAMPLE_WEIGHTS)] = list(
map(label_weight.get, feed_dict[nn.var(LABEL_GOLD)]))
feed_dict[nn.var(TEST_MODE)] = 0
if train_config.input_dropout_keep_prob < 1.:
for _key in [TID_0, TID_1, TID_2]:
var = nn.var(_key)
_tids = feed_dict[var]
feed_dict[var] = tid_dropout(
_tids, train_config.input_dropout_keep_prob)
res = sess.run(fetches=fetches[TRAIN], feed_dict=feed_dict)
labels_predict += res[LABEL_PREDICT].tolist()
labels_gold += dataset[LABEL_GOLD][batch_index].tolist()
labels_predict, labels_gold = labels_predict[:
n_sample], labels_gold[:
n_sample]
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
print_evaluation(res)
eval_history[TRAIN].append(res)
global_step = tf.train.global_step(sess, nn.var(GLOBAL_STEP))
if train_config.valid_rate == 0.:
if best_res[TRAIN] is None or res[
early_stop_metric] > best_res[TRAIN][early_stop_metric]:
best_res[TRAIN] = res
no_update_count[TRAIN] = 0
saver.save(sess,
save_path=model_output_prefix,
global_step=global_step)
else:
no_update_count[TRAIN] += 1
else:
if best_res[TRAIN] is None or res[
early_stop_metric] > best_res[TRAIN][early_stop_metric]:
best_res[TRAIN] = res
no_update_count[TRAIN] = 0
else:
no_update_count[TRAIN] += 1
# 计算在验证集上的表现, 不更新模型参数
print('VALID')
n_sample = index_iterator.n_sample(VALID)
labels_predict = list()
labels_gold = list()
for batch_index in index_iterator.iterate(batch_size,
mode=VALID,
shuffle=False):
feed_dict = {
nn.var(_key): dataset[_key][batch_index]
for _key in feed_key[TEST]
}
feed_dict[nn.var(TEST_MODE)] = 1
res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict)
labels_predict += res[LABEL_PREDICT].tolist()
labels_gold += dataset[LABEL_GOLD][batch_index].tolist()
labels_predict, labels_gold = labels_predict[:
n_sample], labels_gold[:
n_sample]
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
eval_history[DEV].append(res)
print_evaluation(res)
# Early Stop
if best_res[VALID] is None or res[
early_stop_metric] > best_res[VALID][early_stop_metric]:
saver.save(sess,
save_path=model_output_prefix,
global_step=global_step)
best_res[VALID] = res
no_update_count[VALID] = 0
else:
no_update_count[VALID] += 1
# eval test
_mode = TEST
_dataset = datasets[_mode]
_index_iterator = index_iterators[_mode]
_n_sample = _index_iterator.n_sample()
labels_predict = list()
labels_gold = list()
for batch_index in _index_iterator.iterate(batch_size,
shuffle=False):
feed_dict = {
nn.var(_key): _dataset[_key][batch_index]
for _key in feed_key[TEST]
}
feed_dict[nn.var(TEST_MODE)] = 1
res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict)
labels_predict += res[LABEL_PREDICT].tolist()
labels_gold += _dataset[LABEL_GOLD][batch_index].tolist()
labels_predict, labels_gold = labels_predict[:
_n_sample], labels_gold[:
_n_sample]
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
eval_history[TEST].append(res)
if no_update_count[TRAIN] >= max_no_update_count:
break
# 训练结束 ##########################################################################
# 确保输出文件夹存在
print(
'========================= BEST ROUND EVALUATION ========================='
)
json.dump(eval_history,
open(data_config.output_path(output_key, 'eval', 'json'), 'w'))
with tf.Session() as sess:
prefix_checkpoint = tf.train.latest_checkpoint(
data_config.model_path(key=output_key))
saver = tf.train.import_meta_graph('{}.meta'.format(prefix_checkpoint))
saver.restore(sess, prefix_checkpoint)
nn = BaseNNModel(config=None)
nn.set_graph(tf.get_default_graph())
for mode in [TRAIN, TEST]:
dataset = datasets[mode]
index_iterator = index_iterators[mode]
n_sample = index_iterator.n_sample()
prob_predict = list()
labels_predict = list()
labels_gold = list()
hidden_feats = list()
for batch_index in index_iterator.iterate(batch_size,
shuffle=False):
feed_dict = {
nn.var(_key): dataset[_key][batch_index]
for _key in feed_key[TEST]
}
feed_dict[nn.var(TEST_MODE)] = 1
res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict)
prob_predict += res[PROB_PREDICT].tolist()
labels_predict += res[LABEL_PREDICT].tolist()
hidden_feats += res[HIDDEN_FEAT].tolist()
labels_gold += dataset[LABEL_GOLD][batch_index].tolist()
prob_predict = prob_predict[:n_sample]
labels_predict = labels_predict[:n_sample]
labels_gold = labels_gold[:n_sample]
hidden_feats = hidden_feats[:n_sample]
if mode == TEST:
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
best_res[TEST] = res
# 导出隐藏层
with open(data_config.output_path(output_key, mode, HIDDEN_FEAT),
'w') as file_obj:
for _feat in hidden_feats:
file_obj.write('\t'.join(map(str, _feat)) + '\n')
# 导出预测的label
with open(data_config.output_path(output_key, mode, LABEL_PREDICT),
'w') as file_obj:
for _label in labels_predict:
file_obj.write('{}\n'.format(_label))
with open(data_config.output_path(output_key, mode, PROB_PREDICT),
'w') as file_obj:
for _prob in prob_predict:
file_obj.write('\t'.join(map(str, _prob)) + '\n')
for mode in [TRAIN, VALID, TEST]:
if mode == VALID and train_config.valid_rate == 0.:
continue
res = best_res[mode]
print(mode)
print_evaluation(res)
json.dump(
res,
open(data_config.output_path(output_key, mode, EVALUATION),
'w'))
print()
test_score_list = map(lambda _item: _item['f1'], eval_history[TEST])
print('best test f1 reached: {}'.format(max(test_score_list)))
print('OUTPUT_KEY: {}'.format(output_key))
def main(ensemble_mode, config_path='e93.yaml', final_output=None):
"""
[Usage]
python3 -m algo.ensemble93 main -e mv --build-analysis
:param ensemble_mode:
:param config_path:
:param final_output: string
:return:
"""
config_data = yaml.load(open(config_path))
config = Config(data=config_data)
labels_predict = dict()
labels_predict_last = dict()
labels_gold = dict()
n_sample = dict()
modes = {
TRAIN: [TRAIN, TEST],
FINAL: [
FINAL,
]
}
for mode in [TRAIN, FINAL]:
labels = list()
for _mode in modes[mode]:
label_path = data_config.path(_mode, LABEL, None)
labels += load_label_list(label_path)
labels_gold[mode] = labels
n_sample[mode] = len(labels)
output_dim = max(labels_gold[TRAIN]) + 1
if ensemble_mode == SOFT_VOTING:
for mode in [TRAIN, TEST]:
components = dict()
for output_key in config.components:
path = data_config.output_path(output_key, mode, PROB_PREDICT)
prob_list = list()
with open(path) as file_obj:
for line in file_obj:
line = line.strip()
if line == '':
continue
prob = list(map(float, line.split('\t')))
prob_list.append(prob)
components[output_key] = prob_list
labels = list()
for i in range(n_sample[mode]):
prob = np.zeros((output_dim, ))
for output_key, prob_list in components.items():
prob += np.asarray(prob_list[i])
labels.append(np.argmax(prob))
labels_predict[mode] = labels
elif ensemble_mode == MAJORITY_VOTING:
for mode in [TRAIN, FINAL]:
components = list()
for output_key in config.components:
label_list = list()
for _mode in modes[mode]:
path = data_config.output_path(output_key, _mode,
LABEL_PREDICT)
label_list += load_label_list(path)
components.append(label_list)
labels = list()
for i in range(n_sample[mode]):
prob = np.zeros((output_dim, ))
for label_list in components:
label = label_list[i]
prob[label] += 1
labels.append(np.argmax(prob))
labels_predict[mode] = labels
elif ensemble_mode == WEIGHTED_MAJORITY_VOTE:
raise NotImplementedError
else:
raise ValueError('unknown mode: {}'.format(ensemble_mode))
for mode in [TRAIN, FINAL]:
if mode == TRAIN: continue
print('=== {} ==='.format(mode))
res = basic_evaluate(gold=labels_gold[mode], pred=labels_predict[mode])
print(mode)
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print()
n_sample = len(labels_predict[mode])
labels_predict_last[mode] = labels_predict[mode]
# 修正HAS
if config.tri_enabled:
n_changed = 0
votes = [[0 for _ in range(4)] for _ in range(n_sample)]
for output_key in config.tri:
labels = list()
for _mode in modes[mode]:
path = data_config.output_path(output_key, _mode,
LABEL_PREDICT)
labels += load_label_list(path)
if len(labels) != n_sample:
raise Exception('mismatch {}({}) != {}'.format(
output_key, len(labels), n_sample))
for i, label in enumerate(labels):
votes[i][label] += 1
base = list() + labels_predict_last[mode]
for i, vote in enumerate(votes):
if base[i] != 0:
arg_max = int(np.argmax(vote))
if arg_max != 0 and vote[arg_max] >= config.tri_min_vote:
if base[i] != arg_max:
n_changed += 1
base[i] = arg_max
print('n_exchanged within "HAS": {}'.format(n_changed))
labels_predict_last[mode] = base
res = basic_evaluate(gold=labels_gold[mode], pred=base)
print(mode, '(after TRI)')
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print()
# 将判成HAS的样本修正为Others
if config.others_enabled:
votes = [0 for i in range(n_sample)]
n_changed = 0
for output_key in config.others:
labels = list()
for _mode in modes[mode]:
path = data_config.output_path(output_key, _mode,
LABEL_PREDICT)
labels += load_label_list(path)
if len(labels) != n_sample:
raise Exception('mismatch {}({}) != {}'.format(
output_key, len(labels), n_sample))
for i, label in enumerate(labels):
if label == 0:
votes[i] += 1
if config.others_min_vote == 'all':
min_vote = len(config.others)
else:
min_vote = int(config.others_min_vote)
base = list() + labels_predict_last[mode]
for i, vote in enumerate(votes):
if vote >= min_vote:
if base[i] != 0:
n_changed += 1
base[i] = 0
print('n_changed to "OTHERS": {}'.format(n_changed))
labels_predict_last[mode] = base
res = basic_evaluate(gold=labels_gold[mode], pred=base)
print(mode, '(after OTHERS)')
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print()
if mode == FINAL and final_output is not None:
first_line = open(data_config.path_train, 'r').readline()
with open(final_output, 'w') as o_obj:
o_obj.write(first_line)
lines = open(
data_config.path_test_no_labels).read().strip().split('\n')
lines = lines[1:]
lines = list(map(lambda l: l.strip(), lines))
labels = labels_predict_last[FINAL]
labels = list(map(lambda l: label_str[l], labels))
assert len(labels) == len(lines)
for line, label in zip(lines, labels):
o_obj.write('{}\t{}\n'.format(line, label))
def train(model_name,
label_version=None,
label_key=None,
config_path='c93f.yaml',
check=False):
"""
python -m algo.main93_v2 train
python3 -m algo.main93_v2 train -c config_ntua93.yaml
:param model_name: string
:param label_version: string
:param config_path: string
:return:
"""
text_version = 'ek'
config_data = yaml.load(open(config_path))
NNModel = getattr(
importlib.import_module('algo.m93.{}'.format(model_name)), 'NNModel')
output_key = '{}_{}_{}'.format(model_name, text_version, int(time.time()))
if label_key is not None:
output_key = '{}_{}'.format(label_key, output_key)
output_key = 'f_{}'.format(output_key)
print('OUTPUT_KEY: {}'.format(output_key))
# 准备输出路径的文件夹
data_config.prepare_output_folder(output_key=output_key)
data_config.prepare_model_folder(output_key=output_key)
shutil.copy(config_path, data_config.output_path(output_key, ALL, CONFIG))
w2v_key = '{}_{}'.format(config_data['word']['w2v_version'], text_version)
w2v_model_path = data_config.path(ALL, WORD2VEC, w2v_key)
vocab_train_path = data_config.path(TRAIN, VOCAB, text_version)
# 加载字典集
# 在模型中会采用所有模型中支持的词向量, 并为有足够出现次数的单词随机生成词向量
vocab_meta_list = load_vocab_list(vocab_train_path)
vocabs = [
_meta['t'] for _meta in vocab_meta_list
if _meta['tf'] >= config_data['word']['min_tf']
]
# 加载词向量与相关数据
lookup_table, vocab_id_mapping, embedding_dim = load_lookup_table2(
w2v_model_path=w2v_model_path, vocabs=vocabs)
json.dump(
vocab_id_mapping,
open(data_config.output_path(output_key, ALL, VOCAB_ID_MAPPING), 'w'))
# 加载配置
nn_config = NNConfig(config_data)
train_config = TrainConfig(config_data['train'])
early_stop_metric = train_config.early_stop_metric
# 加载训练数据
datasets = dict()
datasets[TRAIN], output_dim = load_dataset(
mode=[TRAIN, TEST],
vocab_id_mapping=vocab_id_mapping,
max_seq_len=nn_config.seq_len,
label_version=label_version,
sampling=train_config.train_sampling,
label_map=train_config.label_map(label_key))
datasets[TEST], _ = load_dataset(
mode=FINAL,
vocab_id_mapping=vocab_id_mapping,
max_seq_len=nn_config.seq_len,
label_version=label_version,
sampling=False,
label_map=train_config.label_map(label_key))
# 初始化数据集的检索
index_iterators = {
TRAIN: IndexIterator.from_dataset(datasets[TRAIN]),
TEST: IndexIterator.from_dataset(datasets[TEST])
}
# 按配置将训练数据切割成训练集和验证集
index_iterators[TRAIN].split_train_valid(train_config.valid_rate)
# 计算各个类的权重
if train_config.use_class_weights:
label_weight = {
# 参考 sklearn 中 class_weight='balanced'的公式, 实验显示效果显着
_label: float(index_iterators[TRAIN].n_sample()) /
(index_iterators[TRAIN].dim * len(_index))
for _label, _index in index_iterators[TRAIN].label_index.items()
}
else:
label_weight = {
_label: 1.
for _label in range(index_iterators[TRAIN].dim)
}
# 基于加载的数据更新配置
nn_config.set_embedding_dim(embedding_dim)
nn_config.set_output_dim(output_dim)
# 搭建神经网络
nn = NNModel(config=nn_config)
nn.build_neural_network(lookup_table=lookup_table)
batch_size = train_config.batch_size
fetches = {
mode: {_key: nn.var(_key)
for _key in fetch_key[mode]}
for mode in [TRAIN, TEST]
}
model_output_prefix = data_config.model_path(key=output_key) + '/model'
best_res = {mode: None for mode in [TRAIN, VALID, TEST]}
no_update_count = {mode: 0 for mode in [TRAIN, VALID]}
max_no_update_count = 10
eval_history = {TRAIN: list(), VALID: list(), TEST: list()}
best_epoch = -1
best_epoch_test = -1
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
dataset = datasets[TRAIN]
index_iterator = index_iterators[TRAIN]
# 训练开始 ##########################################################################
for epoch in range(train_config.epoch):
print('== epoch {} = {} ='.format(epoch, output_key))
# 利用训练集进行训练
print('TRAIN')
n_sample = index_iterator.n_sample(TRAIN)
labels_predict = list()
labels_gold = list()
for batch_index in index_iterator.iterate(batch_size,
mode=TRAIN,
shuffle=True):
feed_dict = {
nn.var(_key): dataset[_key][batch_index]
for _key in feed_key[TRAIN]
}
feed_dict[nn.var(SAMPLE_WEIGHTS)] = list(
map(label_weight.get, feed_dict[nn.var(LABEL_GOLD)]))
feed_dict[nn.var(TEST_MODE)] = 0
res = sess.run(fetches=fetches[TRAIN], feed_dict=feed_dict)
labels_predict += res[LABEL_PREDICT].tolist()
labels_gold += dataset[LABEL_GOLD][batch_index].tolist()
labels_predict, labels_gold = labels_predict[:
n_sample], labels_gold[:
n_sample]
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
print_evaluation(res)
eval_history[TRAIN].append(res)
global_step = tf.train.global_step(sess, nn.var(GLOBAL_STEP))
if train_config.valid_rate == 0.:
if best_epoch <= 10 or (best_res[TRAIN] is None
or res[early_stop_metric] >
best_res[TRAIN][early_stop_metric]):
best_epoch = epoch
best_res[TRAIN] = res
no_update_count[TRAIN] = 0
saver.save(sess,
save_path=model_output_prefix,
global_step=global_step)
else:
no_update_count[TRAIN] += 1
else:
if best_res[TRAIN] is None or res[
early_stop_metric] > best_res[TRAIN][early_stop_metric]:
best_res[TRAIN] = res
no_update_count[TRAIN] = 0
else:
no_update_count[TRAIN] += 1
# 计算在验证集上的表现, 不更新模型参数
print('VALID')
n_sample = index_iterator.n_sample(VALID)
labels_predict = list()
labels_gold = list()
for batch_index in index_iterator.iterate(batch_size,
mode=VALID,
shuffle=False):
feed_dict = {
nn.var(_key): dataset[_key][batch_index]
for _key in feed_key[TEST]
}
feed_dict[nn.var(TEST_MODE)] = 1
res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict)
labels_predict += res[LABEL_PREDICT].tolist()
labels_gold += dataset[LABEL_GOLD][batch_index].tolist()
labels_predict, labels_gold = labels_predict[:
n_sample], labels_gold[:
n_sample]
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
eval_history[VALID].append(res)
print_evaluation(res)
# Early Stop
if best_epoch <= 10 or (best_res[VALID] is None
or res[early_stop_metric] >
best_res[VALID][early_stop_metric]):
best_epoch = epoch
saver.save(sess,
save_path=model_output_prefix,
global_step=global_step)
best_res[VALID] = res
no_update_count[VALID] = 0
else:
no_update_count[VALID] += 1
# eval test
_mode = TEST
_dataset = datasets[_mode]
_index_iterator = SimpleIndexIterator.from_dataset(_dataset)
_n_sample = _index_iterator.n_sample()
labels_predict = list()
labels_gold = list()
for batch_index in _index_iterator.iterate(batch_size,
shuffle=False):
feed_dict = {
nn.var(_key): _dataset[_key][batch_index]
for _key in feed_key[TEST]
}
feed_dict[nn.var(TEST_MODE)] = 1
res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict)
labels_predict += res[LABEL_PREDICT].tolist()
labels_gold += _dataset[LABEL_GOLD][batch_index].tolist()
labels_predict, labels_gold = labels_predict[:
_n_sample], labels_gold[:
_n_sample]
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
eval_history[TEST].append(res)
print('TEST')
print_evaluation(res)
if best_res[TEST] is None or res[F1_SCORE] > best_res[TEST][
F1_SCORE]:
best_res[TEST] = res
best_epoch_test = epoch
if no_update_count[TRAIN] >= max_no_update_count:
break
# 训练结束 ##########################################################################
# 确保输出文件夹存在
print(
'========================= BEST ROUND EVALUATION ========================='
)
json.dump(eval_history,
open(data_config.output_path(output_key, 'eval', 'json'), 'w'))
labels_predict_final = None
labels_gold_final = load_label_list(data_config.path(FINAL, LABEL))
with tf.Session() as sess:
prefix_checkpoint = tf.train.latest_checkpoint(
data_config.model_path(key=output_key))
saver = tf.train.import_meta_graph('{}.meta'.format(prefix_checkpoint))
saver.restore(sess, prefix_checkpoint)
nn = BaseNNModel(config=None)
nn.set_graph(tf.get_default_graph())
for mode in [TRAIN, TEST, FINAL]:
dataset = load_dataset(mode=mode,
vocab_id_mapping=vocab_id_mapping,
max_seq_len=nn_config.seq_len,
with_label=False)
index_iterator = SimpleIndexIterator.from_dataset(dataset)
n_sample = index_iterator.n_sample()
prob_predict = list()
labels_predict = list()
for batch_index in index_iterator.iterate(batch_size,
shuffle=False):
feed_dict = {
nn.var(_key): dataset[_key][batch_index]
for _key in feed_key[TEST]
}
feed_dict[nn.var(TEST_MODE)] = 1
res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict)
prob_predict += res[PROB_PREDICT].tolist()
labels_predict += res[LABEL_PREDICT].tolist()
prob_predict = prob_predict[:n_sample]
labels_predict = labels_predict[:n_sample]
if mode == FINAL:
labels_predict_final = labels_predict
# 导出预测的label
with open(data_config.output_path(output_key, mode, LABEL_PREDICT),
'w') as file_obj:
for _label in labels_predict:
file_obj.write('{}\n'.format(_label))
with open(data_config.output_path(output_key, mode, PROB_PREDICT),
'w') as file_obj:
for _prob in prob_predict:
file_obj.write('\t'.join(map(str, _prob)) + '\n')
print('====== best epoch test: {} ======'.format(best_epoch_test))
for mode in [TRAIN, VALID, TEST]:
if mode == VALID and train_config.valid_rate == 0.:
continue
print(mode)
res = eval_history[mode][best_epoch_test]
print_evaluation(res)
if mode == TEST:
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print(eval_history[TEST][best_epoch_test])
print()
print('====== best epoch valid: {} ======'.format(best_epoch))
for mode in [TRAIN, VALID, TEST]:
if mode == VALID and train_config.valid_rate == 0.:
continue
print(mode)
res = eval_history[mode][best_epoch]
print_evaluation(res)
if mode == TEST:
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
json.dump(
res,
open(data_config.output_path(output_key, mode, EVALUATION), 'w'))
print()
print(eval_history[TEST][best_epoch])
print()
if check:
print('====== label_map check ======')
label_map = train_config.label_map(label_key)
if label_map is not None:
new_gold = list()
new_pred = list()
for g, p in zip(labels_gold_final, labels_predict_final):
if g in label_map:
new_gold.append(label_map[g])
new_pred.append(p)
labels_gold_final = new_gold
labels_predict_final = new_pred
res = basic_evaluate(gold=labels_gold_final, pred=labels_predict_final)
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print('OUTPUT_KEY: {}'.format(output_key))
def train(dataset_key,
text_version,
label_version=None,
config_path='config.yaml'):
"""
python -m algo.main93 train semeval2019_task3_dev -t ek
python3 -m algo.main93 train semeval2019_task3_dev -t ek -c config_ntua93.yaml
:param dataset_key: string
:param text_version: string
:param label_version: string
:param config_path: string
:return:
"""
config_data = yaml.load(open(config_path))
output_key = '{}_{}_{}'.format(config_data['module'].rsplit('.', 1)[1],
text_version, int(time.time()))
if label_version is not None:
output_key = '{}_{}'.format(label_version, output_key)
print('OUTPUT_KEY: {}'.format(output_key))
# 准备输出路径的文件夹
data_config.prepare_output_folder(output_key=output_key)
data_config.prepare_model_folder(output_key=output_key)
shutil.copy(config_path, data_config.output_path(output_key, ALL, CONFIG))
# 根据配置加载模块
module_relative_path = config_data['module']
NNModel = getattr(importlib.import_module(module_relative_path), 'NNModel')
NNConfig = getattr(importlib.import_module(module_relative_path),
'NNConfig')
if config_data['analyzer'] == WORD:
w2v_key = '{}_{}'.format(config_data['word']['w2v_version'],
text_version)
w2v_model_path = data_config.path(ALL, WORD2VEC, w2v_key)
vocab_train_path = data_config.path(TRAIN, VOCAB, text_version)
# 加载字典集
# 在模型中会采用所有模型中支持的词向量, 并为有足够出现次数的单词随机生成词向量
vocab_meta_list = load_vocab_list(vocab_train_path)
# vocab_meta_list += load_vocab_list(semeval2018_task3_date_config.path(TRAIN, VOCAB, text_version))
vocabs = [
_meta['t'] for _meta in vocab_meta_list
if _meta['tf'] >= config_data[WORD]['min_tf']
]
# 加载词向量与相关数据
lookup_table, vocab_id_mapping, embedding_dim = load_lookup_table(
w2v_model_path=w2v_model_path, vocabs=vocabs)
json.dump(
vocab_id_mapping,
open(data_config.output_path(output_key, ALL, VOCAB_ID_MAPPING),
'w'))
max_seq_len = MAX_WORD_SEQ_LEN
elif config_data['analyzer'] == CHAR:
texts = load_text_list(data_config.path(TRAIN, TEXT))
char_set = set()
for text in texts:
char_set |= set(text)
lookup_table, vocab_id_mapping, embedding_dim = build_random_lookup_table(
vocabs=char_set, dim=config_data['char']['embedding_dim'])
max_seq_len = MAX_CHAR_SEQ_LEN
else:
raise ValueError('invalid analyzer: {}'.format(
config_data['analyzer']))
# 加载训练数据
datasets, output_dim = load_dataset(data_config=data_config,
analyzer=config_data['analyzer'],
vocab_id_mapping=vocab_id_mapping,
seq_len=max_seq_len,
with_label=True,
label_version=label_version,
text_version=text_version)
# 加载配置
nn_config = NNConfig(config_data)
train_config = TrainConfig(config_data['train'])
# 初始化数据集的检索
index_iterators = {
mode: IndexIterator(datasets[mode][LABEL_GOLD])
for mode in [TRAIN, TEST]
}
# 按配置将训练数据切割成训练集和验证集
index_iterators[TRAIN].split_train_valid(train_config.valid_rate)
# 计算各个类的权重
if train_config.use_class_weights:
label_weight = {
# 参考 sklearn 中 class_weight='balanced'的公式, 实验显示效果显着
_label: float(index_iterators[TRAIN].n_sample()) /
(index_iterators[TRAIN].dim * len(_index))
for _label, _index in index_iterators[TRAIN].label_index.items()
}
else:
label_weight = {
_label: 1.
for _label in range(index_iterators[TRAIN].dim)
}
# 基于加载的数据更新配置
nn_config.set_embedding_dim(embedding_dim)
nn_config.set_output_dim(output_dim)
nn_config.set_seq_len(max_seq_len)
# 搭建神经网络
nn = NNModel(config=nn_config)
nn.build_neural_network(lookup_table=lookup_table)
batch_size = train_config.batch_size
fetches = {
mode: {_key: nn.var(_key)
for _key in fetch_key[mode]}
for mode in [TRAIN, TEST]
}
last_eval = {TRAIN: None, VALID: None, TEST: None}
model_output_prefix = data_config.model_path(key=output_key) + '/model'
best_res = {mode: None for mode in [TRAIN, VALID]}
no_update_count = {mode: 0 for mode in [TRAIN, VALID]}
max_no_update_count = 10
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables())
dataset = datasets[TRAIN]
index_iterator = index_iterators[TRAIN]
# 训练开始 ##########################################################################
for epoch in range(train_config.epoch):
print('== epoch {} =='.format(epoch))
# 利用训练集进行训练
print('TRAIN')
n_sample = index_iterator.n_sample(TRAIN)
labels_predict = list()
labels_gold = list()
for batch_index in index_iterator.iterate(batch_size,
mode=TRAIN,
shuffle=True):
feed_dict = {
nn.var(_key): dataset[_key][batch_index]
for _key in feed_key[TRAIN]
}
feed_dict[nn.var(SAMPLE_WEIGHTS)] = list(
map(label_weight.get, feed_dict[nn.var(LABEL_GOLD)]))
feed_dict[nn.var(TEST_MODE)] = 0
res = sess.run(fetches=fetches[TRAIN], feed_dict=feed_dict)
labels_predict += res[LABEL_PREDICT].tolist()
labels_gold += dataset[LABEL_GOLD][batch_index].tolist()
labels_predict, labels_gold = labels_predict[:
n_sample], labels_gold[:
n_sample]
labels_predict, labels_gold = labels_predict[:
n_sample], labels_gold[:
n_sample]
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
last_eval[TRAIN] = res
print_evaluation(res)
global_step = tf.train.global_step(sess, nn.var(GLOBAL_STEP))
if train_config.valid_rate == 0.:
if best_res[TRAIN] is None or res[F1_SCORE] > best_res[TRAIN][
F1_SCORE]:
best_res[TRAIN] = res
no_update_count[TRAIN] = 0
saver.save(sess,
save_path=model_output_prefix,
global_step=global_step)
else:
no_update_count[TRAIN] += 1
else:
if best_res[TRAIN] is None or res[F1_SCORE] > best_res[TRAIN][
F1_SCORE]:
best_res[TRAIN] = res
no_update_count[TRAIN] = 0
else:
no_update_count[TRAIN] += 1
# 计算在验证集上的表现, 不更新模型参数
print('VALID')
n_sample = index_iterator.n_sample(VALID)
labels_predict = list()
labels_gold = list()
for batch_index in index_iterator.iterate(batch_size,
mode=VALID,
shuffle=False):
feed_dict = {
nn.var(_key): dataset[_key][batch_index]
for _key in feed_key[TEST]
}
feed_dict[nn.var(TEST_MODE)] = 1
res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict)
labels_predict += res[LABEL_PREDICT].tolist()
labels_gold += dataset[LABEL_GOLD][batch_index].tolist()
labels_predict, labels_gold = labels_predict[:
n_sample], labels_gold[:
n_sample]
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
last_eval[VALID] = res
print_evaluation(res)
# Early Stop
if best_res[VALID] is None or res[F1_SCORE] > best_res[VALID][
F1_SCORE]:
saver.save(sess,
save_path=model_output_prefix,
global_step=global_step)
best_res[VALID] = res
no_update_count[VALID] = 0
else:
no_update_count[VALID] += 1
if no_update_count[TRAIN] >= max_no_update_count:
break
# 训练结束 ##########################################################################
# 确保输出文件夹存在
print(
'========================= BEST ROUND EVALUATION ========================='
)
with tf.Session() as sess:
prefix_checkpoint = tf.train.latest_checkpoint(
data_config.model_path(key=output_key))
saver = tf.train.import_meta_graph('{}.meta'.format(prefix_checkpoint))
saver.restore(sess, prefix_checkpoint)
nn = BaseNNModel(config=None)
nn.set_graph(tf.get_default_graph())
for mode in [TRAIN, TEST]:
dataset = datasets[mode]
index_iterator = index_iterators[mode]
n_sample = index_iterator.n_sample()
prob_predict = list()
labels_predict = list()
labels_gold = list()
hidden_feats = list()
for batch_index in index_iterator.iterate(batch_size,
shuffle=False):
feed_dict = {
nn.var(_key): dataset[_key][batch_index]
for _key in feed_key[TEST]
}
feed_dict[nn.var(TEST_MODE)] = 1
res = sess.run(fetches=fetches[TEST], feed_dict=feed_dict)
prob_predict += res[PROB_PREDICT].tolist()
labels_predict += res[LABEL_PREDICT].tolist()
hidden_feats += res[HIDDEN_FEAT].tolist()
labels_gold += dataset[LABEL_GOLD][batch_index].tolist()
prob_predict = prob_predict[:n_sample]
labels_predict = labels_predict[:n_sample]
labels_gold = labels_gold[:n_sample]
hidden_feats = hidden_feats[:n_sample]
if mode == TEST:
res = basic_evaluate(gold=labels_gold, pred=labels_predict)
best_res[TEST] = res
# 导出隐藏层
with open(data_config.output_path(output_key, mode, HIDDEN_FEAT),
'w') as file_obj:
for _feat in hidden_feats:
file_obj.write('\t'.join(map(str, _feat)) + '\n')
# 导出预测的label
with open(data_config.output_path(output_key, mode, LABEL_PREDICT),
'w') as file_obj:
for _label in labels_predict:
file_obj.write('{}\n'.format(_label))
with open(data_config.output_path(output_key, mode, PROB_PREDICT),
'w') as file_obj:
for _prob in prob_predict:
file_obj.write('\t'.join(map(str, _prob)) + '\n')
for mode in [TRAIN, VALID, TEST]:
if mode == VALID and train_config.valid_rate == 0.:
continue
res = best_res[mode]
print(mode)
print_evaluation(res)
json.dump(
res,
open(data_config.output_path(output_key, mode, EVALUATION),
'w'))
print()
print('OUTPUT_KEY: {}'.format(output_key))
def show_config(output_key):
path = data_config.output_path(output_key, ALL, CONFIG)
print(open(path).read())
print(path)
def main(input_filename, config_path='e93.yaml', final_output=None):
"""
[Usage]
python3 -m algo.ensemble93 main -e mv --build-analysis
"""
config_data = yaml.load(open(config_path))
config = Config(data=config_data)
labels_gold = dict()
labels_predict = dict()
labels_predict_last = dict()
dataset = Processor.load_origin(input_filename)
labels_predict[FINAL] = list(map(lambda _item: _item[-1], dataset))
for mode in [
FINAL,
]:
if not mode == FINAL:
res = basic_evaluate(gold=labels_gold[mode],
pred=labels_predict[mode])
print(mode)
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print()
n_sample = len(labels_predict[mode])
labels_predict_last[mode] = labels_predict[mode]
# 修正HAS
if config.tri_enabled:
n_changed = 0
votes = [[0 for _ in range(4)] for _ in range(n_sample)]
for output_key in config.tri:
labels = list()
for _mode in modes[mode]:
path = data_config.output_path(output_key, _mode,
LABEL_PREDICT)
labels += load_label_list(path)
if len(labels) != n_sample:
raise Exception('mismatch {}({}) != {}'.format(
output_key, len(labels), n_sample))
for i, label in enumerate(labels):
votes[i][label] += 1
base = list() + labels_predict_last[mode]
for i, vote in enumerate(votes):
arg_max = int(np.argmax(vote))
if arg_max == 0:
continue
if base[i] != 0:
if vote[arg_max] >= config.tri_min_vote:
if base[i] != arg_max:
n_changed += 1
base[i] = arg_max
elif vote[arg_max] >= config.tri_out_vote:
base[i] = arg_max
n_changed += 1
print('n_exchanged within "HAS": {}'.format(n_changed))
labels_predict_last[mode] = base
if not mode == FINAL:
res = basic_evaluate(gold=labels_gold[mode], pred=base)
print(mode, '(after TRI)')
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print()
# 将判成HAS的样本修正为Others
if config.others_enabled:
votes = [0 for _ in range(n_sample)]
n_changed = 0
for output_key in config.others:
labels = list()
for _mode in modes[mode]:
path = data_config.output_path(output_key, _mode,
LABEL_PREDICT)
labels += load_label_list(path)
if len(labels) != n_sample:
raise Exception('mismatch {}({}) != {}'.format(
output_key, len(labels), n_sample))
for i, label in enumerate(labels):
if label == 0:
votes[i] += 1
if config.others_min_vote == 'all':
min_vote = len(config.others)
else:
min_vote = int(config.others_min_vote)
base = list() + labels_predict_last[mode]
for i, vote in enumerate(votes):
if vote >= min_vote:
if base[i] != 0:
n_changed += 1
base[i] = 0
print('n_changed to "OTHERS": {}'.format(n_changed))
labels_predict_last[mode] = base
if not mode == FINAL:
res = basic_evaluate(gold=labels_gold[mode], pred=base)
print(mode, '(after OTHERS)')
print_evaluation(res)
for col in res[CONFUSION_MATRIX]:
print(','.join(map(str, col)))
print()
if mode == FINAL and final_output is not None:
labels = labels_predict_last[FINAL]
export_final(final_output, labels)
