fastNLP提供方便的数据预处理，训练和测试模型的功能
fastNLP用DataSet和Instance保存和处理数据。每个DataSet表示一个数据集，每个Instance表示一个数据样本。一个DataSet存有多个Instance，每个Instance可以自定义存哪些内容。
有一些read_*方法，可以轻松从文件读取数据，存成DataSet。
from fastNLP import DataSet
from fastNLP import Instance
# 从csv读取数据到DataSet
win_path = "C:\\Users\zyfeng\Desktop\FudanNLP\\fastNLP\\test\\data_for_tests\\tutorial_sample_dataset.csv"
dataset = DataSet.read_csv(win_path, headers=('raw_sentence', 'label'), sep='\t')
print(dataset[0]){'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story ., 'label': 1}
# DataSet.append(Instance)加入新数据
dataset.append(Instance(raw_sentence='fake data', label='0'))
dataset[-1]{'raw_sentence': fake data, 'label': 0}
# DataSet.apply(func, new_field_name)对数据预处理
# 将所有数字转为小写
dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')
# label转int
dataset.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True)
# 使用空格分割句子
dataset.drop(lambda x: len(x['raw_sentence'].split()) == 0)
def split_sent(ins):
return ins['raw_sentence'].split()
dataset.apply(split_sent, new_field_name='words', is_input=True)# DataSet.drop(func)筛除数据
# 删除低于某个长度的词语
dataset.drop(lambda x: len(x['words']) <= 3)# 分出测试集、训练集
test_data, train_data = dataset.split(0.3)
print("Train size: ", len(test_data))
print("Test size: ", len(train_data))Train size: 54 Test size:
fastNLP中的Vocabulary轻松构建词表，将词转成数字
from fastNLP import Vocabulary
# 构建词表, Vocabulary.add(word)
vocab = Vocabulary(min_freq=2)
train_data.apply(lambda x: [vocab.add(word) for word in x['words']])
vocab.build_vocab()
# index句子, Vocabulary.to_index(word)
train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)
test_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)
print(test_data[0]){'raw_sentence': the plot is romantic comedy boilerplate from start to finish ., 'label': 2, 'label_seq': 2, 'words': ['the', 'plot', 'is', 'romantic', 'comedy', 'boilerplate', 'from', 'start', 'to', 'finish', '.'], 'word_seq': [2, 13, 9, 24, 25, 26, 15, 27, 11, 28, 3]}
# 假设你们需要做强化学习或者gan之类的项目，也许你们可以使用这里的dataset
from fastNLP.core.batch import Batch
from fastNLP.core.sampler import RandomSampler
batch_iterator = Batch(dataset=train_data, batch_size=2, sampler=RandomSampler())
for batch_x, batch_y in batch_iterator:
print("batch_x has: ", batch_x)
print("batch_y has: ", batch_y)
break- batch_x has: {'words': array([list(['this', 'kind', 'of', 'hands-on', 'storytelling', 'is', 'ultimately', 'what', 'makes', 'shanghai', 'ghetto', 'move', 'beyond', 'a', 'good', ',', 'dry', ',', 'reliable', 'textbook', 'and', 'what', 'allows', 'it', 'to', 'rank', 'with', 'its', 'worthy', 'predecessors', '.']),
list(['the', 'entire', 'movie', 'is', 'filled', 'with', 'deja', 'vu', 'moments', '.'])],
- dtype=object), 'word_seq': tensor([[ 19, 184, 6, 1, 481, 9, 206, 50, 91, 1210, 1609, 1330,
- 495, 5, 63, 4, 1269, 4, 1, 1184, 7, 50, 1050, 10,
8, 1611, 16, 21, 1039, 1, 2],
- [ 3, 711, 22, 9, 1282, 16, 2482, 2483, 200, 2, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]])}
batch_y has: {'label_seq': tensor([3, 2])}
# 定义一个简单的Pytorch模型
from fastNLP.models import CNNText
model = CNNText(embed_num=len(vocab), embed_dim=50, num_classes=5, padding=2, dropout=0.1)
model- CNNText(
- (embed): Embedding(
(embed): Embedding(77, 50, padding_idx=0) (dropout): Dropout(p=0.0)
) (conv_pool): ConvMaxpool( (convs): ModuleList( (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,)) (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,)) (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,)) ) ) (dropout): Dropout(p=0.1) (fc): Linear( (linear): Linear(in_features=12, out_features=5, bias=True) )
)
使用fastNLP的Trainer训练模型
from fastNLP import Trainer
from copy import deepcopy
from fastNLP import CrossEntropyLoss
from fastNLP import AccuracyMetric# 进行overfitting测试
copy_model = deepcopy(model)
overfit_trainer = Trainer(model=copy_model,
train_data=test_data,
dev_data=test_data,
loss=CrossEntropyLoss(pred="output", target="label_seq"),
metrics=AccuracyMetric(),
n_epochs=10,
save_path=None)
overfit_trainer.train()training epochs started 2018-12-07 14:07:20
HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…
Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.037037 Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.296296 Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.333333 Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.555556 Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.611111 Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.481481 Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.62963 Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.685185 Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.722222 Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.777778
# 实例化Trainer，传入模型和数据，进行训练
trainer = Trainer(model=model,
train_data=train_data,
dev_data=test_data,
loss=CrossEntropyLoss(pred="output", target="label_seq"),
metrics=AccuracyMetric(),
n_epochs=5)
trainer.train()
print('Train finished!')training epochs started 2018-12-07 14:08:10
HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=5), HTML(value='')), layout=Layout(display='i…
Epoch 1/5. Step:1/5. AccuracyMetric: acc=0.037037 Epoch 2/5. Step:2/5. AccuracyMetric: acc=0.037037 Epoch 3/5. Step:3/5. AccuracyMetric: acc=0.037037 Epoch 4/5. Step:4/5. AccuracyMetric: acc=0.185185 Epoch 5/5. Step:5/5. AccuracyMetric: acc=0.240741 Train finished!
from fastNLP import Tester
tester = Tester(data=test_data, model=model, metrics=AccuracyMetric())
acc = tester.test()[tester] AccuracyMetric: acc=0.240741
['raw_sentence', 'word_seq1', 'word_seq2', 'raw_label','label']
通过
DataSet.set_input('word_seq1', word_seq2', flag=True)将'word_seq1', 'word_seq2'设置为input
通过
DataSet.set_target('label', flag=True)将'label'设置为targetclass Model(nn.Module):
def __init__(self):
xxx
def forward(self, word_seq1, word_seq2):
# (1) 这里使用的形参名必须和DataSet中的input field的名称对应。因为我们是通过形参名, 进行赋值的
# (2) input field的数量可以多于这里的形参数量。但是不能少于。
xxxx
# 输出必须是一个dict(1) 从DataSet中按照batch_size取出一个batch，调用Model.forward
(2) 将 Model.forward的结果 与 标记为target的field 传入Losser当中。
由于每个人写的Model.forward的output的dict可能key并不一样，比如有人是{'pred':xxx}, {'output': xxx};
另外每个人将target可能也会设置为不同的名称, 比如有人是label, 有人设置为target；
为了解决以上的问题，我们的loss提供映射机制
比如CrossEntropyLosser的需要的输入是(prediction, target)。但是forward的output是{'output': xxx}; 'label'是target
那么初始化losser的时候写为CrossEntropyLosser(prediction='output', target='label')即可
(3) 对于Metric是同理的
Metric计算也是从 forward的结果中取值 与 设置target的field中取值。 也是可以通过映射找到对应的值 只有被设置为input或者target的数据才会在train的过程中被取出来
(1.1) 我们只会在设置为input的field中寻找传递给Model.forward的参数。
(1.2) 我们在传递值给losser或者metric的时候会使用来自:
(a)Model.forward的output
(b)被设置为target的field(1.1) 构建模型过程中，
例如:
DataSet中x，seq_lens是input，那么forward就应该是
def forward(self, x, seq_lens):
pass
我们是通过形参名称进行匹配的field的(2.1) 处理过程中将某些field设置为input，某些field设置为target(3.1) 构建模型过程中，需要注意forward函数的形参名需要和DataSet中设置为input的field名称是一致的。
例如:
DataSet中x，seq_lens是input，那么forward就应该是
def forward(self, x, seq_lens):
pass
我们是通过形参名称进行匹配的field的
(3.2) 模型的forward的output需要是dict类型的。
建议将输出设置为{"pred": xx}.