def test_init(self):
fields = {"x": [1, 2, 3], "y": [4, 5, 6]}
ins = Instance(x=[1, 2, 3], y=[4, 5, 6])
self.assertTrue(isinstance(ins.fields, dict))
self.assertEqual(ins.fields, fields)
ins = Instance(**fields)
self.assertEqual(ins.fields, fields)
def convert_for_infer(self, data, vocabs):
for word_seq in data:
# list
x = TextField(word_seq, is_target=False)
instance = Instance()
instance.add_field("word_seq", x)
self.append(instance)
self.index_field("word_seq", vocabs["word_vocab"])
def test_list_of_numpy_to_tensor(self):
ds = DataSet([Instance(x=np.array([1, 2]), y=np.array([3, 4])) for _ in range(2)] +
[Instance(x=np.array([1, 2, 3, 4]), y=np.array([3, 4, 5, 6])) for _ in range(2)])
ds.set_input("x")
ds.set_target("y")
iter = Batch(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False)
for x, y in iter:
print(x, y)
def formatRowString(self, msg):
msg = msg.strip()
tokenized_char = [x for x in msg]
self._dataset = DataSet()
if self._addTarget2Vocab:
ins = Instance(chars=tokenized_char,
raw_chars=tokenized_char,
target=list(dict(self._target_vocab).keys()))
else:
ins = Instance(chars=tokenized_char, raw_chars=tokenized_char)
self._dataset.append(ins)
def test_list_of_list_to_tensor(self):
ds = DataSet([Instance(x=[1, 2], y=[3, 4]) for _ in range(2)] +
[Instance(x=[1, 2, 3, 4], y=[3, 4, 5, 6]) for _ in range(2)])
ds.set_input("x")
ds.set_target("y")
iter = Batch(ds, batch_size=4, sampler=SequentialSampler(), as_numpy=False)
for x, y in iter:
self.assertTrue(isinstance(x["x"], torch.Tensor))
self.assertEqual(tuple(x["x"].shape), (4, 4))
self.assertTrue(isinstance(y["y"], torch.Tensor))
self.assertEqual(tuple(y["y"].shape), (4, 4))
def prepare_fake_dataset():
mean = np.array([-3, -3])
cov = np.array([[1, 0], [0, 1]])
class_A = np.random.multivariate_normal(mean, cov, size=(1000,))
mean = np.array([3, 3])
cov = np.array([[1, 0], [0, 1]])
class_B = np.random.multivariate_normal(mean, cov, size=(1000,))
data_set = DataSet([Instance(x=[float(item[0]), float(item[1])], y=[0.0]) for item in class_A] +
[Instance(x=[float(item[0]), float(item[1])], y=[1.0]) for item in class_B])
return data_set
def convert(self, data):
"""Convert a 3D list to a DataSet object.
:param data: A 3D tensor.
Example::
[
[ [premise_word_11, premise_word_12, ...], [hypothesis_word_11, hypothesis_word_12, ...], [label_1] ],
[ [premise_word_21, premise_word_22, ...], [hypothesis_word_21, hypothesis_word_22, ...], [label_2] ],
...
]
:return: A DataSet object.
"""
data_set = DataSet()
for example in data:
p, h, l = example
# list, list, str
instance = Instance()
instance.add_field("premise", p)
instance.add_field("hypothesis", h)
instance.add_field("truth", l)
data_set.append(instance)
data_set.apply(lambda ins: len(ins["premise"]), new_field_name="premise_len")
data_set.apply(lambda ins: len(ins["hypothesis"]), new_field_name="hypothesis_len")
data_set.set_input("premise", "hypothesis", "premise_len", "hypothesis_len")
data_set.set_target("truth")
return data_set
def test(self):
data = DataSet()
for text, label in zip(texts, labels):
x = TextField(text, is_target=False)
y = LabelField(label, is_target=True)
ins = Instance(text=x, label=y)
data.append(ins)
# use vocabulary to index data
data.index_field("text", vocab)
# define naive sampler for batch class
class SeqSampler:
def __call__(self, dataset):
return list(range(len(dataset)))
# use batch to iterate dataset
data_iterator = Batch(data, 2, SeqSampler(), False)
total_data = 0
for batch_x, batch_y in data_iterator:
total_data += batch_x["text"].size(0)
self.assertTrue(batch_x["text"].size(0) == 2
or total_data == len(raw_texts))
self.assertTrue(isinstance(batch_x, dict))
self.assertTrue(isinstance(batch_x["text"], torch.LongTensor))
self.assertTrue(isinstance(batch_y, dict))
self.assertTrue(isinstance(batch_y["label"], torch.LongTensor))
def test_case_1(self):
args = {
"epochs": 3,
"batch_size": 2,
"validate": False,
"use_cuda": False,
"pickle_path": "./save/",
"save_best_dev": True,
"model_name": "default_model_name.pkl",
"loss": Loss("cross_entropy"),
"optimizer": Optimizer("Adam", lr=0.001, weight_decay=0),
"vocab_size": 10,
"word_emb_dim": 100,
"rnn_hidden_units": 100,
"num_classes": 5,
"evaluator": SeqLabelEvaluator()
}
trainer = SeqLabelTrainer(**args)
train_data = [
[['a', 'b', 'c', 'd', 'e'], ['a', '@', 'c', 'd', 'e']],
[['a', '@', 'c', 'd', 'e'], ['a', '@', 'c', 'd', 'e']],
[['a', 'b', '#', 'd', 'e'], ['a', '@', 'c', 'd', 'e']],
[['a', 'b', 'c', '?', 'e'], ['a', '@', 'c', 'd', 'e']],
[['a', 'b', 'c', 'd', '$'], ['a', '@', 'c', 'd', 'e']],
[['!', 'b', 'c', 'd', 'e'], ['a', '@', 'c', 'd', 'e']],
]
vocab = {
'a': 0,
'b': 1,
'c': 2,
'd': 3,
'e': 4,
'!': 5,
'@': 6,
'#': 7,
'$': 8,
'?': 9
}
label_vocab = {'a': 0, '@': 1, 'c': 2, 'd': 3, 'e': 4}
data_set = DataSet()
for example in train_data:
text, label = example[0], example[1]
x = TextField(text, False)
x_len = LabelField(len(text), is_target=False)
y = TextField(label, is_target=False)
ins = Instance(word_seq=x, truth=y, word_seq_origin_len=x_len)
data_set.append(ins)
data_set.index_field("word_seq", vocab)
data_set.index_field("truth", label_vocab)
model = SeqLabeling(args)
trainer.train(network=model, train_data=data_set, dev_data=data_set)
# If this can run, everything is OK.
os.system("rm -rf save")
print("pickle path deleted")
def __getitem__(self, idx):
"""Fetch Instance(s) at the `idx` position(s) in the dataset.
Notice: This method returns a copy of the actual instance(s). Any change to the returned value would not modify
the origin instance(s) of the DataSet.
If you want to make in-place changes to all Instances, use `apply` method.
:param idx: can be int or slice.
:return: If `idx` is int, return an Instance object.
If `idx` is slice, return a DataSet object.
"""
if isinstance(idx, int):
return Instance(**{
name: self.field_arrays[name][idx]
for name in self.field_arrays
})
elif isinstance(idx, slice):
if idx.start is not None and (idx.start >= len(self)
or idx.start <= -len(self)):
raise RuntimeError(
f"Start index {idx.start} out of range 0-{len(self)-1}")
data_set = DataSet()
for field in self.field_arrays.values():
data_set.add_field(name=field.name,
fields=field.content[idx],
padding_val=field.padding_val,
is_input=field.is_input,
is_target=field.is_target)
return data_set
else:
raise KeyError(
"Unrecognized type {} for idx in __getitem__ method".format(
type(idx)))
def load(self, path):
datalist = []
with open(path, 'r', encoding='utf-8') as f:
sample = []
for line in f:
if line.startswith('\n'):
datalist.append(sample)
sample = []
elif line.startswith('#'):
continue
else:
sample.append(line.split('\t'))
if len(sample) > 0:
datalist.append(sample)
data = [self.get_one(sample) for sample in datalist]
data_list = list(filter(lambda x: x is not None, data))
ds = DataSet()
for example in data_list:
ds.append(
Instance(words=example[0],
pos_tags=example[1],
heads=example[2],
labels=example[3]))
return ds
def _load(self, path):
ds = DataSet()
for idx, data in _read_conll(path,
indexes=self.indexes,
dropna=self.dropna):
# if data[0][0] == '#':
# data[0] = data[0][1:]
# data[1] = data[1][1:]
for i in range(len(self.headers)):
if data[i][0].startswith('NE-'):
data[i] = data[i][1:]
if 'TOKEN' in data[i][0]:
data[i] = data[i][1:]
# print(data) #data[1] = iob(list(data[1]))
doc_start = False
for i, h in enumerate(self.headers):
field = data[i]
if str(' '.join(list(field))).startswith(' #'):
continue
if str(field[0]).startswith('-DOCSTART-'):
doc_start = True
break
if doc_start:
continue
ins = {h: data[i] for i, h in enumerate(self.headers)}
ds.append(Instance(**ins))
if len(ds) == 0:
raise RuntimeError("No data found {}.".format(path))
return ds
def _load(self, path: str = None):
logging.info(path)
ds = DataSet()
with open(path, 'r', encoding='utf-8') as f:
for line in f:
if line == '': continue
splits = line.strip().split('\t')
if len(splits) == 4:
raw_targets = [int(i) for i in splits[3].strip().lstrip('[').rstrip(']').split(' ')]
elif len(splits) == 3:
raw_targets = [0, 0, 0, 0, 0]
else:
logging.error('data format error')
raw_query = splits[0]
raw_entity = splits[1]
left_context = raw_query[0:raw_query.find(raw_entity)]
right_context = raw_query[raw_query.find(raw_entity) + len(raw_entity):]
if left_context == '': left_context = '-'
if right_context == '': right_context = '-'
raw_entity_label = splits[2]
if left_context and right_context and raw_entity and raw_entity_label:
ds.append(
Instance(left_context=tokenize(left_context),
right_context=tokenize(right_context),
raw_entity=tokenize(raw_entity),
raw_entity_label=entity_label_tokenize(raw_entity_label),
target=raw_targets))
return ds
def load(self, filepath, in_word_splitter=None, cut_long_sent=False):
if in_word_splitter is None:
in_word_splitter = self.in_word_splitter
dataset = DataSet()
with open(filepath, 'r') as f:
words = []
for line in f:
line = line.strip()
if len(line) == 0: # new line
if len(words) == 0: # 不能接受空行
continue
line = ' '.join(words)
if cut_long_sent:
sents = cut_long_sentence(line)
else:
sents = [line]
for sent in sents:
instance = Instance(raw_sentence=sent)
dataset.append(instance)
words = []
else:
line = line.split()[0]
if in_word_splitter is None:
words.append(line)
else:
words.append(line.split(in_word_splitter)[0])
return dataset
def test_append(self):
dd = DataSet()
for _ in range(3):
dd.append(Instance(x=[1, 2, 3, 4], y=[5, 6]))
self.assertEqual(len(dd), 3)
self.assertEqual(dd.field_arrays["x"].content, [[1, 2, 3, 4]] * 3)
self.assertEqual(dd.field_arrays["y"].content, [[5, 6]] * 3)
def load(self, filepath, in_word_splitter=None, cut_long_sent=False):
"""
允许使用的情况有(默认以\t或空格作为seg)
这是 fastNLP , 一个 非常 good 的 包 .
和
也/D 在/P 團員/Na 之中/Ng ，/COMMACATEGORY
如果splitter不为None则认为是第二种情况, 且我们会按splitter分割"也/D", 然后取第一部分. 例如"也/D".split('/')[0]
:param filepath:
:param in_word_splitter:
:return:
"""
if in_word_splitter == None:
in_word_splitter = self.in_word_splitter
dataset = DataSet()
with open(filepath, 'r') as f:
for line in f:
line = line.strip()
if len(line.replace(' ', '')) == 0: # 不能接受空行
continue
if not in_word_splitter is None:
words = []
for part in line.split():
word = part.split(in_word_splitter)[0]
words.append(word)
line = ' '.join(words)
if cut_long_sent:
sents = cut_long_sentence(line)
else:
sents = [line]
for sent in sents:
instance = Instance(raw_sentence=sent)
dataset.append(instance)
return dataset
def load(self, path, cut_long_sent=False):
datalist = []
with open(path, 'r', encoding='utf-8') as f:
sample = []
for line in f:
if line.startswith('\n'):
datalist.append(sample)
sample = []
elif line.startswith('#'):
continue
else:
sample.append(line.split('\t'))
if len(sample) > 0:
datalist.append(sample)
ds = DataSet()
for sample in datalist:
# print(sample)
res = self.get_one(sample)
if res is None:
continue
line = ' '.join(res)
if cut_long_sent:
sents = cut_long_sentence(line)
else:
sents = [line]
for raw_sentence in sents:
ds.append(Instance(raw_sentence=raw_sentence))
return ds
def load(self, path):
datalist = []
with open(path, 'r', encoding='utf-8') as f:
sample = []
for line in f:
if line.startswith('\n'):
datalist.append(sample)
sample = []
elif line.startswith('#'):
continue
else:
sample.append(line.split('\t'))
if len(sample) > 0:
datalist.append(sample)
ds = DataSet(name='conll')
for sample in datalist:
# print(sample)
res = self.get_one(sample)
ds.append(
Instance(word_seq=TextField(res[0], is_target=False),
pos_seq=TextField(res[1], is_target=False),
head_indices=SeqLabelField(res[2], is_target=True),
head_labels=TextField(res[3], is_target=True)))
return ds
def convert(self, data):
data_set = DataSet()
for item in data:
sent_words = item[0]
if self.pos is True and self.ner is True:
instance = Instance(words=sent_words,
pos_tags=item[1],
ner=item[2])
elif self.pos is True:
instance = Instance(words=sent_words, pos_tags=item[1])
elif self.ner is True:
instance = Instance(words=sent_words, ner=item[1])
else:
instance = Instance(words=sent_words)
data_set.append(instance)
data_set.apply(lambda ins: len(ins["words"]), new_field_name="seq_len")
return data_set
def test(self):
data = DataSet()
for text in texts:
x = TextField(text, is_target=False)
ins = Instance(text=x)
data.append(ins)
data_set = create_dataset_from_lists(texts, vocab, has_target=False)
self.assertTrue(type(data) == type(data_set))
def load(self, path):
"""
返回的DataSet, 包含以下的field
words：list of str,
tag: list of str, 被加入了BMES tag, 比如原来的序列为['VP', 'NN', 'NN', ..]，会被认为是["S-VP", "B-NN", "M-NN",..]
假定了输入为conll的格式，以空行隔开两个句子，每行共7列，即
::
1 编者按 编者按 NN O 11 nmod:topic
2 ： ： PU O 11 punct
3 7月 7月 NT DATE 4 compound:nn
4 12日 12日 NT DATE 11 nmod:tmod
5 ， ， PU O 11 punct
1 这 这 DT O 3 det
2 款 款 M O 1 mark:clf
3 飞行 飞行 NN O 8 nsubj
4 从 从 P O 5 case
5 外型 外型 NN O 8 nmod:prep
"""
datalist = []
with open(path, 'r', encoding='utf-8') as f:
sample = []
for line in f:
if line.startswith('\n'):
datalist.append(sample)
sample = []
elif line.startswith('#'):
continue
else:
sample.append(line.split('\t'))
if len(sample) > 0:
datalist.append(sample)
ds = DataSet()
for sample in datalist:
# print(sample)
res = self.get_one(sample)
if res is None:
continue
char_seq = []
pos_seq = []
for word, tag in zip(res[0], res[1]):
char_seq.extend(list(word))
if len(word) == 1:
pos_seq.append('S-{}'.format(tag))
elif len(word) > 1:
pos_seq.append('B-{}'.format(tag))
for _ in range(len(word) - 2):
pos_seq.append('M-{}'.format(tag))
pos_seq.append('E-{}'.format(tag))
else:
raise ValueError("Zero length of word detected.")
ds.append(Instance(words=char_seq, tag=pos_seq))
return ds
def test_init_v1(self):
ds = DataSet([Instance(x=[1, 2, 3, 4], y=[5, 6])] * 40)
self.assertTrue("x" in ds.field_arrays and "y" in ds.field_arrays)
self.assertEqual(ds.field_arrays["x"].content, [
[1, 2, 3, 4],
] * 40)
self.assertEqual(ds.field_arrays["y"].content, [
[5, 6],
] * 40)
def convert(self, data):
data_set = DataSet()
for item in data:
sent_words, sent_pos_tag = item[0], item[1]
data_set.append(Instance(words=sent_words, tags=sent_pos_tag))
data_set.apply(lambda ins: len(ins), new_field_name="seq_len")
data_set.set_target("tags")
data_set.set_input("sent_words")
data_set.set_input("seq_len")
return data_set
def convert_seq2seq_dataset(data):
"""Convert list of data into DataSet
:param data: list of list of strings, [num_examples, *].
::
[
[ [word_11, word_12, ...], [label_1, label_1, ...] ],
[ [word_21, word_22, ...], [label_2, label_1, ...] ],
...
]
:return: a DataSet.
"""
dataset = DataSet()
for sample in data:
word_seq, label_seq = sample[0], sample[1]
ins = Instance()
ins.add_field("word_seq", TextField(word_seq, is_target=False)) \
.add_field("label_seq", TextField(label_seq, is_target=True))
dataset.append(ins)
return dataset
def construct_dataset(sentences):
"""Construct a data set from a list of sentences.
:param sentences: list of list of str
:return dataset: a DataSet object
"""
dataset = DataSet()
for sentence in sentences:
instance = Instance()
instance['raw_sentence'] = sentence
dataset.append(instance)
return dataset
def convert_with_vocabs(self, data, vocabs):
for example in data:
word_seq, label_seq = example[0], example[1]
# list, list
x = TextField(word_seq, is_target=False)
x_len = LabelField(len(word_seq), is_target=False)
y = TextField(label_seq, is_target=False)
instance = Instance()
instance.add_field("word_seq", x)
instance.add_field("truth", y)
instance.add_field("word_seq_origin_len", x_len)
self.append(instance)
self.index_field("word_seq", vocabs["word_vocab"])
self.index_field("truth", vocabs["label_vocab"])
def convert(self, parsed_data):
dataset = DataSet()
for sample in parsed_data:
label0_list = list(map(lambda labels: labels[0], sample[1]))
label1_list = list(map(lambda labels: labels[1], sample[1]))
label2_list = list(map(lambda labels: labels[2], sample[1]))
dataset.append(
Instance(token_list=sample[0],
label0_list=label0_list,
label1_list=label1_list,
label2_list=label2_list))
return dataset
def convert(data):
dataset = DataSet()
for sample in data:
word_seq = [BOS] + sample['words']
pos_seq = [BOS] + sample['pos_tags']
heads = [0] + sample['heads']
head_tags = [BOS] + sample['labels']
dataset.append(
Instance(raw_words=word_seq,
pos=pos_seq,
gold_heads=heads,
arc_true=heads,
tags=head_tags))
return dataset
def convert(data):
dataset = DataSet()
for sample in data:
word_seq = [BOS] + sample[0]
pos_seq = [BOS] + sample[1]
heads = [0] + list(map(int, sample[2]))
head_tags = [BOS] + sample[3]
dataset.append(
Instance(words=word_seq,
pos=pos_seq,
gold_heads=heads,
arc_true=heads,
tags=head_tags))
return dataset
def convert(self, data):
dataset = DataSet()
for sample in data:
word_seq = [BOS] + sample[0] + [EOS]
pos_seq = [BOS] + sample[1] + [EOS]
heads = [0] + list(map(int, sample[2])) + [0]
head_tags = [BOS] + sample[3] + [EOS]
dataset.append(
Instance(word_seq=TextField(word_seq, is_target=False),
pos_seq=TextField(pos_seq, is_target=False),
gold_heads=SeqLabelField(heads, is_target=False),
head_indices=SeqLabelField(heads, is_target=True),
head_labels=TextField(head_tags, is_target=True)))
return dataset
