fastNLP上手教程

fastNLP提供方便的数据预处理,训练和测试模型的功能

DataSet & Instance

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:

Vocabulary

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])}

Model

# 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)
  )
)

Trainer & Tester

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

In summary

fastNLP Trainer的伪代码逻辑

1. 准备DataSet,假设DataSet中共有如下的fields

['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)'label'设置为target

2. 初始化模型

class 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

3. Trainer的训练过程

(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

一些问题.

1. DataSet中为什么需要设置input和target

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

2. 我们是通过forwad中的形参名将DataSet中的field赋值给对应的参数

(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

1. 加载数据到DataSet

2. 使用apply操作对DataSet进行预处理

(2.1) 处理过程中将某些field设置为input,某些field设置为target

3. 构建模型

(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}.