Dataframe as datasource in torchtext - dataframe

Question

I have a dataframe, which has two columns (review and sentiment). I am using pytorch and torchtext library for preprocessing data.
Is it possible to use dataframe as source to read data from, in torchtext?
I am looking for something similar to, but not
data.TabularDataset.splits(path='./data')
I have performed some operation (clean, change to required format) on data and final data is in a dataframe.
If not torchtext, what other package would you suggest that would help in preprocessing text data present in a datarame. I could not find anything online. Any help would be great.

Answer 1

Adapting the Dataset and Example classes from 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
Then, first define fields using torchtext.data fields. For example:
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 }
before simply loading the dataframes:
train_ds = DataFrameDataset(train_df, fields)
valid_ds = DataFrameDataset(valid_df, fields)

Answer 2

Thanks Geoffrey.
From looking at the source code for torchtext.data.field
https://pytorch.org/text/_modules/torchtext/data/field.html
It looks like the 'train' parameter needs to be either a Dataset already, or some iterable source of text data. But given we haven't created a dataset at this point I am guessing you have passed in just the column of text from the dataframe.