import json
from overrides import overrides
import torch
from claf.data import utils
from claf.data.collate import PadCollator
from claf.data.dataset.base import DatasetBase
[docs]class SeqClsDataset(DatasetBase):
"""
Dataset for Sequence Classification
* Args:
batch: Batch DTO (claf.data.batch)
* Kwargs:
helper: helper from data_reader
"""
def __init__(self, batch, vocab, helper=None):
super(SeqClsDataset, self).__init__()
self.name = "seq_cls"
self.vocab = vocab
self.helper = helper
self.class_idx2text = helper["class_idx2text"]
self.sequences = {feature["id"]: feature["sequence"]["text"] for feature in batch.features}
# Features
self.sequence_idxs = [feature["sequence"] for feature in batch.features]
self.features = [self.sequence_idxs] # for lazy evaluation
# Labels
self.data_ids = {data_index: label["id"] for (data_index, label) in enumerate(batch.labels)}
self.data_indices = list(self.data_ids.keys())
self.classes = {
label["id"]: {
"class_idx": label["class_idx"],
"class_text": label["class_text"],
}
for label in batch.labels
}
self.class_text = [label["class_text"] for label in batch.labels]
self.class_idx = [label["class_idx"] for label in batch.labels]
[docs] @overrides
def collate_fn(self, cuda_device_id=None):
""" collate: indexed features and labels -> tensor """
collator = PadCollator(cuda_device_id=cuda_device_id, pad_value=self.vocab.pad_index)
def make_tensor_fn(data):
data_idxs, sequence_idxs, class_idxs = zip(*data)
features = {
"sequence": utils.transpose(sequence_idxs, skip_keys=["text"]),
}
labels = {
"class_idx": class_idxs,
"data_idx": data_idxs,
}
return collator(features, labels)
return make_tensor_fn
@overrides
def __getitem__(self, index):
self.lazy_evaluation(index)
return (
self.data_indices[index],
self.sequence_idxs[index],
self.class_idx[index],
)
def __len__(self):
return len(self.data_ids)
def __repr__(self):
dataset_properties = {
"name": self.name,
"total_count": self.__len__(),
"num_classes": self.num_classes,
"sequence_maxlen": self.sequence_maxlen,
"classes": self.class_idx2text,
}
return json.dumps(dataset_properties, indent=4)
@property
def num_classes(self):
return len(self.class_idx2text)
@property
def sequence_maxlen(self):
return self._get_feature_maxlen(self.sequence_idxs)
[docs] def get_id(self, data_index):
return self.data_ids[data_index]
[docs] @overrides
def get_ground_truth(self, data_id):
return self.classes[data_id]
[docs] def get_class_text_with_idx(self, class_index):
if class_index is None:
raise ValueError("class_index is required.")
return self.class_idx2text[class_index]