import math
import numpy as np
import torch
from torchtext.data.field import Field, RawField
from torchtext.data.iterator import BucketIterator
from torchtext.data.pipeline import Pipeline
from torchtext.vocab import Vectors
from datasets.castor_dataset import CastorPairDataset
def get_class_probs(sim, *args):
"""
Convert a single label into class probabilities.
"""
class_probs = np.zeros(SICK.NUM_CLASSES)
ceil, floor = math.ceil(sim), math.floor(sim)
if ceil == floor:
class_probs[floor - 1] = 1
else:
class_probs[floor - 1] = ceil - sim
class_probs[ceil - 1] = sim - floor
return class_probs
class SICK(CastorPairDataset):
NAME = 'sick'
NUM_CLASSES = 5
ID_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
TEXT_FIELD = Field(batch_first=True, tokenize=lambda x: x) # tokenizer is identity since we already tokenized it to compute external features
EXT_FEATS_FIELD = Field(tensor_type=torch.FloatTensor, use_vocab=False, batch_first=True, tokenize=lambda x: x)
LABEL_FIELD = Field(sequential=False, tensor_type=torch.FloatTensor, use_vocab=False, batch_first=True, postprocessing=Pipeline(get_class_probs))
RAW_TEXT_FIELD = RawField()
@staticmethod
def sort_key(ex):
return len(ex.sentence_1)
def __init__(self, path):
"""
Create a SICK dataset instance
"""
super().__init__(path)
@classmethod
def splits(cls, path, train='train', validation='dev', test='test', **kwargs):
return super(SICK, cls).splits(path, train=train, validation=validation, test=test, **kwargs)
@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 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)
