import os
import re
import numpy as np
import torch
from torchtext.data import NestedField, Field, TabularDataset
from torchtext.data.iterator import BucketIterator
from torchtext.vocab import Vectors
from datasets.reuters import clean_string, split_sents
def char_quantize(string, max_length=1000):
identity = np.identity(len(Yelp2014CharQuantized.ALPHABET))
quantized_string = np.array([identity[Yelp2014CharQuantized.ALPHABET[char]] for char in list(string.lower()) if char in Yelp2014CharQuantized.ALPHABET], dtype=np.float32)
if len(quantized_string) > max_length:
return quantized_string[:max_length]
else:
return np.concatenate((quantized_string, np.zeros((max_length - len(quantized_string), len(Yelp2014CharQuantized.ALPHABET)), dtype=np.float32)))
def process_labels(string):
"""
Returns the label string as a list of integers
:param string:
:return:
"""
return [float(x) for x in string]
class Yelp2014(TabularDataset):
NAME = 'Yelp2014'
NUM_CLASSES = 5
IS_MULTILABEL = False
TEXT_FIELD = Field(batch_first=True, tokenize=clean_string, include_lengths=True)
LABEL_FIELD = Field(sequential=False, use_vocab=False, batch_first=True, preprocessing=process_labels)
@staticmethod
def sort_key(ex):
return len(ex.text)
@classmethod
def splits(cls, path, train=os.path.join('Yelp2014', 'train.tsv'),
validation=os.path.join('Yelp2014', 'dev.tsv'),
test=os.path.join('Yelp2014', 'test.tsv'), **kwargs):
return super(Yelp2014, cls).splits(
path, train=train, validation=validation, test=test,
format='tsv', fields=[('label', cls.LABEL_FIELD), ('text', cls.TEXT_FIELD)]
)
@classmethod
def iters(cls, path, vectors_name, vectors_cache, batch_size=64, shuffle=True, device=0, vectors=None,
unk_init=torch.Tensor.zero_):
"""
:param path: directory containing train, test, dev files
:param vectors_name: name of word vectors file
:param vectors_cache: path to directory containing word vectors file
:param batch_size: batch size
:param device: GPU device
:param vectors: custom vectors - either predefined torchtext vectors or your own custom Vector classes
:param unk_init: function used to generate vector for OOV words
:return:
"""
if vectors is None:
vectors = Vectors(name=vectors_name, cache=vectors_cache, unk_init=unk_init)
train, val, test = cls.splits(path)
cls.TEXT_FIELD.build_vocab(train, val, test, vectors=vectors)
return BucketIterator.splits((train, val, test), batch_size=batch_size, repeat=False, shuffle=shuffle,
sort_within_batch=True, device=device)
class Yelp2014CharQuantized(Yelp2014):
ALPHABET = dict(map(lambda t: (t[1], t[0]), enumerate(list("""abcdefghijklmnopqrstuvwxyz0123456789,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}"""))))
TEXT_FIELD = Field(sequential=False, use_vocab=False, batch_first=True, preprocessing=char_quantize)
@classmethod
def iters(cls, path, vectors_name, vectors_cache, batch_size=64, shuffle=True, device=0, vectors=None,
unk_init=torch.Tensor.zero_):
"""
:param path: directory containing train, test, dev files
:param batch_size: batch size
:param device: GPU device
:return:
"""
train, val, test = cls.splits(path)
return BucketIterator.splits((train, val, test), batch_size=batch_size, repeat=False, shuffle=shuffle, device=device)
class Yelp2014Hierarchical(Yelp2014):
NESTING_FIELD = Field(batch_first=True, tokenize=clean_string)
TEXT_FIELD = NestedField(NESTING_FIELD, tokenize=split_sents)
