数据框作为torchtext中的数据源

Question

我有一个数据框，其中有两列（评论和情感）。我正在使用pytorch和torchtext库预处理数据。是否可以使用数据帧作为源以torchtext读取数据？我正在寻找类似但不是的东西

data.TabularDataset.splits(path='./data')

我已经对数据执行了一些操作（干净，更改为所需的格式），最终数据在数据框中。

如果不是torchtext，那么您建议使用什么其他软件包来帮助预处理dataram中存在的文本数据。我在网上找不到任何东西。任何帮助都会很棒。

Answer 1

适应Dataset和Example类来自torchtext.data

    from torchtext.data import Field, Dataset, Example
    import pandas as pd

     class DataFrameDataset(Dataset):
         """Class for using pandas DataFrames as a datasource"""
         def __init__(self, examples, fields, filter_pred=None):
             """
             Create a dataset from a pandas dataframe of examples and Fields
             Arguments:
                 examples pd.DataFrame: DataFrame of examples
                 fields {str: Field}: The Fields to use in this tuple. The
                     string is a field name, and the Field is the associated field.
                 filter_pred (callable or None): use only exanples for which
                     filter_pred(example) is true, or use all examples if None.
                     Default is None
             """
             self.examples = examples.apply(SeriesExample.fromSeries, args=(fields,), axis=1).tolist()
             if filter_pred is not None:
                 self.examples = filter(filter_pred, self.examples)
             self.fields = dict(fields)
             # Unpack field tuples
             for n, f in list(self.fields.items()):
                 if isinstance(n, tuple):
                     self.fields.update(zip(n, f))
                     del self.fields[n]

     class SeriesExample(Example):
         """Class to convert a pandas Series to an Example"""
        
         @classmethod
         def fromSeries(cls, data, fields):
             return cls.fromdict(data.to_dict(), fields)

         @classmethod
         def fromdict(cls, data, fields):
             ex = cls()
             
             for key, field in fields.items():
                 if key not in data:
                     raise ValueError("Specified key {} was not found in "
                     "the input data".format(key))
                 if field is not None:
                     setattr(ex, key, field.preprocess(data[key]))
                 else:
                     setattr(ex, key, data[key])
             return ex

然后，首先定义fieldsusingtorchtext.data字段。例如：

    TEXT = data.Field(tokenize='spacy')
    LABEL = data.LabelField(dtype=torch.float)
    TEXT.build_vocab(train, max_size=25000, vectors="glove.6B.100d") 
    LABEL.build_vocab(train)
    fields = { 'sentiment' : LABEL, 'review' : TEXT }

在简单地加载数据帧之前：

    train_ds = DataFrameDataset(train_df, fields)
    valid_ds = DataFrameDataset(valid_df, fields)